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    "## What's this TensorFlow business?\n",
    "\n",
    "You've written a lot of code in this assignment to provide a whole host of neural network functionality. Dropout, Batch Norm, and 2D convolutions are some of the workhorses of deep learning in computer vision. You've also worked hard to make your code efficient and vectorized.\n",
    "\n",
    "For the last part of this assignment, though, we're going to leave behind your beautiful codebase and instead migrate to one of two popular deep learning frameworks: in this instance, TensorFlow (or PyTorch, if you switch over to that notebook)\n",
    "\n",
    "#### What is it?\n",
    "TensorFlow is a system for executing computational graphs over Tensor objects, with native support for performing backpropogation for its Variables. In it, we work with Tensors which are n-dimensional arrays analogous to the numpy ndarray.\n",
    "\n",
    "#### Why?\n",
    "\n",
    "* Our code will now run on GPUs! Much faster training. Writing your own modules to run on GPUs is beyond the scope of this class, unfortunately.\n",
    "* We want you to be ready to use one of these frameworks for your project so you can experiment more efficiently than if you were writing every feature you want to use by hand. \n",
    "* We want you to stand on the shoulders of giants! TensorFlow and PyTorch are both excellent frameworks that will make your lives a lot easier, and now that you understand their guts, you are free to use them :) \n",
    "* We want you to be exposed to the sort of deep learning code you might run into in academia or industry. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How will I learn TensorFlow?\n",
    "\n",
    "TensorFlow has many excellent tutorials available, including those from [Google themselves](https://www.tensorflow.org/get_started/get_started).\n",
    "\n",
    "Otherwise, this notebook will walk you through much of what you need to do to train models in TensorFlow. See the end of the notebook for some links to helpful tutorials if you want to learn more or need further clarification on topics that aren't fully explained here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets\n"
   ]
  },
  {
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      "/root/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import math\n",
    "import timeit\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "    # Subsample the data\n",
    "    mask = range(num_training, num_training + num_validation)\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = range(num_training)\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = range(num_test)\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Model\n",
    "\n",
    "### Some useful utilities\n",
    "\n",
    ". Remember that our image data is initially N x H x W x C, where:\n",
    "* N is the number of datapoints\n",
    "* H is the height of each image in pixels\n",
    "* W is the height of each image in pixels\n",
    "* C is the number of channels (usually 3: R, G, B)\n",
    "\n",
    "This is the right way to represent the data when we are doing something like a 2D convolution, which needs spatial understanding of where the pixels are relative to each other. When we input image data into fully connected affine layers, however, we want each data example to be represented by a single vector -- it's no longer useful to segregate the different channels, rows, and columns of the data."
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The example model itself\n",
    "\n",
    "The first step to training your own model is defining its architecture.\n",
    "\n",
    "Here's an example of a convolutional neural network defined in TensorFlow -- try to understand what each line is doing, remembering that each layer is composed upon the previous layer. We haven't trained anything yet - that'll come next - for now, we want you to understand how everything gets set up. \n",
    "\n",
    "In that example, you see 2D convolutional layers (Conv2d), ReLU activations, and fully-connected layers (Linear). You also see the Hinge loss function, and the Adam optimizer being used. \n",
    "\n",
    "Make sure you understand why the parameters of the Linear layer are 5408 and 10.\n",
    "\n",
    "### TensorFlow Details\n",
    "In TensorFlow, much like in our previous notebooks, we'll first specifically initialize our variables, and then our network model."
   ]
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    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# setup input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "def simple_model(X,y):\n",
    "    # define our weights (e.g. init_two_layer_convnet)\n",
    "    \n",
    "    # setup variables\n",
    "    Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "    bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "    W1 = tf.get_variable(\"W1\", shape=[5408, 10])\n",
    "    b1 = tf.get_variable(\"b1\", shape=[10])\n",
    "\n",
    "    # define our graph (e.g. two_layer_convnet)\n",
    "    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1\n",
    "    h1 = tf.nn.relu(a1)\n",
    "    h1_flat = tf.reshape(h1,[-1,5408])\n",
    "    y_out = tf.matmul(h1_flat,W1) + b1\n",
    "    return y_out\n",
    "\n",
    "y_out = simple_model(X,y)\n",
    "\n",
    "# define our loss\n",
    "total_loss = tf.losses.hinge_loss(tf.one_hot(y,10),logits=y_out)\n",
    "mean_loss = tf.reduce_mean(total_loss)\n",
    "\n",
    "# define our optimizer\n",
    "optimizer = tf.train.AdamOptimizer(5e-4) # select optimizer and set learning rate\n",
    "train_step = optimizer.minimize(mean_loss)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow supports many other layer types, loss functions, and optimizers - you will experiment with these next. Here's the official API documentation for these (if any of the parameters used above were unclear, this resource will also be helpful). \n",
    "\n",
    "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n",
    "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers\n",
    "* BatchNorm: https://www.tensorflow.org/api_docs/python/tf/layers/batch_normalization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model on one epoch\n",
    "While we have defined a graph of operations above, in order to execute TensorFlow Graphs, by feeding them input data and computing the results, we first need to create a `tf.Session` object. A session encapsulates the control and state of the TensorFlow runtime. For more information, see the TensorFlow [Getting started](https://www.tensorflow.org/get_started/get_started) guide.\n",
    "\n",
    "Optionally we can also specify a device context such as `/cpu:0` or `/gpu:0`. For documentation on this behavior see [this TensorFlow guide](https://www.tensorflow.org/tutorials/using_gpu)\n",
    "\n",
    "You should see a validation loss of around 0.4 to 0.6 and an accuracy of 0.30 to 0.35 below"
   ]
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     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 15.5 and accuracy of 0.094\n"
     ]
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-cadb3e21a481>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_variables_initializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Training'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m         \u001b[0mrun_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_out\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmean_loss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Validation'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m         \u001b[0mrun_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_out\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmean_loss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_val\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-cadb3e21a481>\u001b[0m in \u001b[0;36mrun_model\u001b[0;34m(session, predict, loss_val, Xd, yd, epochs, batch_size, print_every, training, plot_losses)\u001b[0m\n\u001b[1;32m     39\u001b[0m             \u001b[0;31m# have tensorflow compute loss and correct predictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0;31m# and (if given) perform a training step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;31m# aggregate performance stats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1120\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1317\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1318\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1321\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1322\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1324\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1325\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1300\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1301\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "def run_model(session, predict, loss_val, Xd, yd,\n",
    "              epochs=1, batch_size=64, print_every=100,\n",
    "              training=None, plot_losses=False):\n",
    "    # have tensorflow compute accuracy\n",
    "    correct_prediction = tf.equal(tf.argmax(predict,1), y)\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "    \n",
    "    # shuffle indicies\n",
    "    train_indicies = np.arange(Xd.shape[0])\n",
    "    np.random.shuffle(train_indicies)\n",
    "\n",
    "    training_now = training is not None\n",
    "    \n",
    "    # setting up variables we want to compute (and optimizing)\n",
    "    # if we have a training function, add that to things we compute\n",
    "    variables = [mean_loss,correct_prediction,accuracy]\n",
    "    if training_now:\n",
    "        variables[-1] = training\n",
    "    \n",
    "    # counter \n",
    "    iter_cnt = 0\n",
    "    for e in range(epochs):\n",
    "        # keep track of losses and accuracy\n",
    "        correct = 0\n",
    "        losses = []\n",
    "        # make sure we iterate over the dataset once\n",
    "        for i in range(int(math.ceil(Xd.shape[0]/batch_size))):\n",
    "            # generate indicies for the batch\n",
    "            start_idx = (i*batch_size)%Xd.shape[0]\n",
    "            idx = train_indicies[start_idx:start_idx+batch_size]\n",
    "            \n",
    "            # create a feed dictionary for this batch\n",
    "            feed_dict = {X: Xd[idx,:],\n",
    "                         y: yd[idx],\n",
    "                         is_training: training_now }\n",
    "            # get batch size\n",
    "            actual_batch_size = yd[idx].shape[0]\n",
    "            \n",
    "            # have tensorflow compute loss and correct predictions\n",
    "            # and (if given) perform a training step\n",
    "            loss, corr, _ = session.run(variables,feed_dict=feed_dict)\n",
    "            \n",
    "            # aggregate performance stats\n",
    "            losses.append(loss*actual_batch_size)\n",
    "            correct += np.sum(corr)\n",
    "            \n",
    "            # print every now and then\n",
    "            if training_now and (iter_cnt % print_every) == 0:\n",
    "                print(\"Iteration {0}: with minibatch training loss = {1:.3g} and accuracy of {2:.2g}\"\\\n",
    "                      .format(iter_cnt,loss,np.sum(corr)/actual_batch_size))\n",
    "            iter_cnt += 1\n",
    "        total_correct = correct/Xd.shape[0]\n",
    "        total_loss = np.sum(losses)/Xd.shape[0]\n",
    "        print(\"Epoch {2}, Overall loss = {0:.3g} and accuracy of {1:.3g}\"\\\n",
    "              .format(total_loss,total_correct,e+1))\n",
    "        if plot_losses:\n",
    "            plt.plot(losses)\n",
    "            plt.grid(True)\n",
    "            plt.title('Epoch {} Loss'.format(e+1))\n",
    "            plt.xlabel('minibatch number')\n",
    "            plt.ylabel('minibatch loss')\n",
    "            plt.show()\n",
    "    return total_loss,total_correct\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/gpu:0\"): #\"/cpu:0\" or \"/gpu:0\" \n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        print('Training')\n",
    "        run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step,True)\n",
    "        print('Validation')\n",
    "        run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a specific model\n",
    "\n",
    "In this section, we're going to specify a model for you to construct. The goal here isn't to get good performance (that'll be next), but instead to get comfortable with understanding the TensorFlow documentation and configuring your own model. \n",
    "\n",
    "Using the code provided above as guidance, and using the following TensorFlow documentation, specify a model with the following architecture:\n",
    "\n",
    "* 7x7 Convolutional Layer with 32 filters and stride of 1\n",
    "* ReLU Activation Layer\n",
    "* Spatial Batch Normalization Layer (trainable parameters, with scale and centering)\n",
    "* 2x2 Max Pooling layer with a stride of 2\n",
    "* Affine layer with 1024 output units\n",
    "* ReLU Activation Layer\n",
    "* Affine layer from 1024 input units to 10 outputs\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clear old variables\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# define our input (e.g. the data that changes every batch)\n",
    "# The first dim is None, and gets sets automatically based on batch size fed in\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "# define model\n",
    "def complex_model(X,y,is_training):\n",
    "    # define our weights (e.g. init_two_layer_convnet)\n",
    "    \n",
    "    # setup variables\n",
    "    #conv layer\n",
    "    Wconv1 = tf.get_variable(\"Wconv1\", shape=[7, 7, 3, 32])\n",
    "    bconv1 = tf.get_variable(\"bconv1\", shape=[32])\n",
    "    #affine1 layer\n",
    "    W1 = tf.get_variable(\"W1\", shape=[5408, 1024])\n",
    "    b1 = tf.get_variable(\"b1\", shape=[1024])\n",
    "    #affine2 layer\n",
    "    W2 = tf.get_variable(\"W2\", shape=[1024, 10])\n",
    "    b2 = tf.get_variable(\"b2\", shape=[10])\n",
    "\n",
    "    # define our graph (e.g. two_layer_convnet)\n",
    "    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,1,1,1], padding='VALID') + bconv1\n",
    "    h1 = tf.nn.relu(a1)\n",
    "    #spatial batch normalization\n",
    "    num_filters=32\n",
    "    # compute on which dimensions\n",
    "    axes = [0,1,2]   \n",
    "    mean,var=tf.nn.moments(h1,axes=axes)\n",
    "    offset = tf.Variable(tf.zeros([num_filters]))\n",
    "    scale = tf.Variable(tf.ones([num_filters]))\n",
    "    eps=0.0001\n",
    "    bn1=tf.nn.batch_normalization(h1,mean,var,offset=offset,scale=scale,variance_epsilon=eps)\n",
    "    \n",
    "    pool1=tf.nn.max_pool(bn1,ksize=[1,2,2,1], strides=[1,2,2,1],padding='SAME')\n",
    "    p1_flat = tf.reshape(pool1,[-1,5408])\n",
    "    y1_out = tf.matmul(p1_flat,W1) + b1\n",
    "    \n",
    "    h2 = tf.nn.relu(y1_out)\n",
    "    h2_flat = tf.reshape(h2,[-1,1024])\n",
    "    y2_out = tf.matmul(h2_flat,W2) + b2\n",
    "    \n",
    "    return y2_out\n",
    "\n",
    "y_out = complex_model(X,y,is_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure you're doing the right thing, use the following tool to check the dimensionality of your output (it should be 64 x 10, since our batches have size 64 and the output of the final affine layer should be 10, corresponding to our 10 classes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 ms ± 173 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "(64, 10)\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Now we're going to feed a random batch into the model \n",
    "# and make sure the output is the right size\n",
    "x = np.random.randn(64, 32, 32,3)\n",
    "with tf.Session() as sess:\n",
    "    with tf.device(\"/cpu:0\"): #\"/cpu:0\" or \"/gpu:0\"\n",
    "        tf.global_variables_initializer().run()\n",
    "\n",
    "        ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "        %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "        print(ans.shape)\n",
    "        print(np.array_equal(ans.shape, np.array([64, 10])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see the following from the run above \n",
    "\n",
    "`(64, 10)`\n",
    "\n",
    "`True`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU!\n",
    "\n",
    "Now, we're going to try and start the model under the GPU device, the rest of the code stays unchanged and all our variables and operations will be computed using accelerated code paths. However, if there is no GPU, we get a Python exception and have to rebuild our graph. On a dual-core CPU, you might see around 50-80ms/batch running the above, while the Google Cloud GPUs (run below) should be around 2-5ms/batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.98 ms ± 211 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    with tf.Session() as sess:\n",
    "        with tf.device(\"/gpu:0\") as dev: #\"/cpu:0\" or \"/gpu:0\"\n",
    "            tf.global_variables_initializer().run()\n",
    "\n",
    "            ans = sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "            %timeit sess.run(y_out,feed_dict={X:x,is_training:True})\n",
    "except tf.errors.InvalidArgumentError:\n",
    "    print(\"no gpu found, please use Google Cloud if you want GPU acceleration\")    \n",
    "    # rebuild the graph\n",
    "    # trying to start a GPU throws an exception \n",
    "    # and also trashes the original graph\n",
    "    tf.reset_default_graph()\n",
    "    X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "    y = tf.placeholder(tf.int64, [None])\n",
    "    is_training = tf.placeholder(tf.bool)\n",
    "    y_out = complex_model(X,y,is_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should observe that even a simple forward pass like this is significantly faster on the GPU. So for the rest of the assignment (and when you go train your models in assignment 3 and your project!), you should use GPU devices. However, with TensorFlow, the default device is a GPU if one is available, and a CPU otherwise, so we can skip the device specification from now on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model.\n",
    "\n",
    "Now that you've seen how to define a model and do a single forward pass of some data through it, let's  walk through how you'd actually train one whole epoch over your training data (using the complex_model you created provided above).\n",
    "\n",
    "Make sure you understand how each TensorFlow function used below corresponds to what you implemented in your custom neural network implementation.\n",
    "\n",
    "First, set up an **RMSprop optimizer** (using a 1e-3 learning rate) and a **cross-entropy loss** function. See the TensorFlow documentation for more information\n",
    "* Layers, Activations, Loss functions : https://www.tensorflow.org/api_guides/python/nn\n",
    "* Optimizers: https://www.tensorflow.org/api_guides/python/train#Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Inputs\n",
    "#     y_out: is what your model computes\n",
    "#     y: is your TensorFlow variable with label information\n",
    "# Outputs\n",
    "#    mean_loss: a TensorFlow variable (scalar) with numerical loss\n",
    "#    optimizer: a TensorFlow optimizer\n",
    "# This should be ~3 lines of code!\n",
    "mean_loss = None\n",
    "optimizer = None\n",
    "total_loss = tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(y,10),logits=y_out)\n",
    "mean_loss = tf.reduce_mean(total_loss)\n",
    "optimizer=tf.train.RMSPropOptimizer(1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# batch normalization in tensorflow requires this extra dependency\n",
    "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(extra_update_ops):\n",
    "    train_step = optimizer.minimize(mean_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model\n",
    "Below we'll create a session and train the model over one epoch. You should see a loss of 1.4 to 2.0 and an accuracy of 0.4 to 0.5. There will be some variation due to random seeds and differences in initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Iteration 0: with minibatch training loss = 3.8 and accuracy of 0.031\n",
      "Iteration 100: with minibatch training loss = 2 and accuracy of 0.48\n",
      "Iteration 200: with minibatch training loss = 1.51 and accuracy of 0.48\n",
      "Iteration 300: with minibatch training loss = 1.38 and accuracy of 0.47\n",
      "Iteration 400: with minibatch training loss = 1.82 and accuracy of 0.42\n",
      "Iteration 500: with minibatch training loss = 1.56 and accuracy of 0.41\n",
      "Iteration 600: with minibatch training loss = 1.34 and accuracy of 0.55\n",
      "Iteration 700: with minibatch training loss = 1.33 and accuracy of 0.53\n",
      "Epoch 1, Overall loss = 1.67 and accuracy of 0.451\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.6726088448154683, 0.45071428571428573)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "\n",
    "sess.run(tf.global_variables_initializer())\n",
    "print('Training')\n",
    "run_model(sess,y_out,mean_loss,X_train,y_train,1,64,100,train_step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the accuracy of the model.\n",
    "\n",
    "Let's see the train and test code in action -- feel free to use these methods when evaluating the models you develop below. You should see a loss of 1.3 to 2.0 with an accuracy of 0.45 to 0.55."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.36 and accuracy of 0.536\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1.3592667026519776, 0.53600000000000003)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Validation')\n",
    "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a _great_ model on CIFAR-10!\n",
    "\n",
    "Now it's your job to experiment with architectures, hyperparameters, loss functions, and optimizers to train a model that achieves ** >= 70% accuracy on the validation set** of CIFAR-10. You can use the `run_model` function from above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Things you should try:\n",
    "- **Filter size**: Above we used 7x7; this makes pretty pictures but smaller filters may be more efficient\n",
    "- **Number of filters**: Above we used 32 filters. Do more or fewer do better?\n",
    "- **Pooling vs Strided Convolution**: Do you use max pooling or just stride convolutions?\n",
    "- **Batch normalization**: Try adding spatial batch normalization after convolution layers and vanilla batch normalization after affine layers. Do your networks train faster?\n",
    "- **Network architecture**: The network above has two layers of trainable parameters. Can you do better with a deep network? Good architectures to try include:\n",
    "    - [conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n",
    "    - [conv-relu-conv-relu-pool]xN -> [affine]xM -> [softmax or SVM]\n",
    "    - [batchnorm-relu-conv]xN -> [affine]xM -> [softmax or SVM]\n",
    "- **Use TensorFlow Scope**: Use TensorFlow scope and/or [tf.layers](https://www.tensorflow.org/api_docs/python/tf/layers) to make it easier to write deeper networks. See [this tutorial](https://www.tensorflow.org/tutorials/layers) for how to use `tf.layers`. \n",
    "- **Use Learning Rate Decay**: [As the notes point out](http://cs231n.github.io/neural-networks-3/#anneal), decaying the learning rate might help the model converge. Feel free to decay every epoch, when loss doesn't change over an entire epoch, or any other heuristic you find appropriate. See the [Tensorflow documentation](https://www.tensorflow.org/versions/master/api_guides/python/train#Decaying_the_learning_rate) for learning rate decay.\n",
    "- **Global Average Pooling**: Instead of flattening and then having multiple affine layers, perform convolutions until your image gets small (7x7 or so) and then perform an average pooling operation to get to a 1x1 image picture (1, 1 , Filter#), which is then reshaped into a (Filter#) vector. This is used in [Google's Inception Network](https://arxiv.org/abs/1512.00567) (See Table 1 for their architecture).\n",
    "- **Regularization**: Add l2 weight regularization, or perhaps use [Dropout as in the TensorFlow MNIST tutorial](https://www.tensorflow.org/get_started/mnist/pros)\n",
    "\n",
    "### Tips for training\n",
    "For each network architecture that you try, you should tune the learning rate and regularization strength. When doing this there are a couple important things to keep in mind:\n",
    "\n",
    "- If the parameters are working well, you should see improvement within a few hundred iterations\n",
    "- Remember the coarse-to-fine approach for hyperparameter tuning: start by testing a large range of hyperparameters for just a few training iterations to find the combinations of parameters that are working at all.\n",
    "- Once you have found some sets of parameters that seem to work, search more finely around these parameters. You may need to train for more epochs.\n",
    "- You should use the validation set for hyperparameter search, and we'll save the test set for evaluating your architecture on the best parameters as selected by the validation set.\n",
    "\n",
    "### Going above and beyond\n",
    "If you are feeling adventurous there are many other features you can implement to try and improve your performance. You are **not required** to implement any of these; however they would be good things to try for extra credit.\n",
    "\n",
    "- Alternative update steps: For the assignment we implemented SGD+momentum, RMSprop, and Adam; you could try alternatives like AdaGrad or AdaDelta.\n",
    "- Alternative activation functions such as leaky ReLU, parametric ReLU, ELU, or MaxOut.\n",
    "- Model ensembles\n",
    "- Data augmentation\n",
    "- New Architectures\n",
    "  - [ResNets](https://arxiv.org/abs/1512.03385) where the input from the previous layer is added to the output.\n",
    "  - [DenseNets](https://arxiv.org/abs/1608.06993) where inputs into previous layers are concatenated together.\n",
    "  - [This blog has an in-depth overview](https://chatbotslife.com/resnets-highwaynets-and-densenets-oh-my-9bb15918ee32)\n",
    "\n",
    "If you do decide to implement something extra, clearly describe it in the \"Extra Credit Description\" cell below.\n",
    "\n",
    "### What we expect\n",
    "At the very least, you should be able to train a ConvNet that gets at **>= 70% accuracy on the validation set**. This is just a lower bound - if you are careful it should be possible to get accuracies much higher than that! Extra credit points will be awarded for particularly high-scoring models or unique approaches.\n",
    "\n",
    "You should use the space below to experiment and train your network. The final cell in this notebook should contain the training and validation set accuracies for your final trained network.\n",
    "\n",
    "Have fun and happy training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feel free to play with this cell\n",
    "\n",
    "def conv33_relu_batch(inputs,num_filter):\n",
    "    regularizer = tf.contrib.layers.l2_regularizer(scale=0.2)\n",
    "    conv1 = tf.layers.conv2d(\n",
    "      inputs=inputs,\n",
    "      filters=num_filter,\n",
    "      strides=[1,1],\n",
    "      kernel_size=[3, 3],\n",
    "      padding=\"same\",\n",
    "      kernel_regularizer=regularizer)\n",
    "    relu1=tf.nn.relu(conv1)\n",
    "    batch_norm1=tf.layers.batch_normalization(relu1,axis=1)\n",
    "    dropout1 = tf.layers.dropout(inputs=batch_norm1, rate=0.5)\n",
    "    conv2 = tf.layers.conv2d(\n",
    "      inputs=dropout1,\n",
    "    #when filters=2*num_filters,it achieve results follows just after 20 epochs\n",
    "        #val:(0.82898137760162349, 0.85199999999999998)\n",
    "        #test:(0.93189846858978276, 0.82850000000000001)\n",
    "        \n",
    "      filters=num_filter,\n",
    "      strides=[1,1],\n",
    "      kernel_size=[3, 3],\n",
    "      padding=\"same\",\n",
    "      kernel_regularizer=regularizer)\n",
    "    relu2=tf.nn.relu(conv2)\n",
    "    batch_norm2=tf.layers.batch_normalization(relu2,axis=1)\n",
    "    pool= tf.layers.max_pooling2d(inputs=batch_norm2, pool_size=[2, 2], strides=2)\n",
    "    dropout2 = tf.layers.dropout(inputs=pool, rate=0.5)\n",
    "    return dropout2\n",
    "\n",
    "def my_model(X,y,is_training):\n",
    "    #structure\n",
    "    #[conv-relu-conv-relu] -> global average pooling -> [softmax]\n",
    "    num_classes=10\n",
    "    # define our graph use layer API\n",
    "    nn1=conv33_relu_batch(X,64)\n",
    "    nn2=conv33_relu_batch(nn1,128)\n",
    "    nn3=conv33_relu_batch(nn2,256)\n",
    "    #global average pooling\n",
    "    pool_size=(nn3.shape[1],nn3.shape[2])\n",
    "    pool_ave = tf.layers.average_pooling2d(\n",
    "      inputs=nn3,\n",
    "      pool_size=pool_size,\n",
    "      strides=[1,1],\n",
    "      padding='valid',\n",
    "      data_format='channels_last')\n",
    "    pool_ave_flat_size=pool_ave.shape[1]*pool_ave.shape[2]*pool_ave.shape[3]\n",
    "    pool_ave_flat = tf.reshape(pool_ave, [-1,pool_ave_flat_size])\n",
    "    dropout = tf.layers.dropout(inputs=pool_ave_flat, rate=0.5, training=is_training)\n",
    "    \n",
    "    y_out= tf.layers.dense(inputs=dropout, units=num_classes)\n",
    "    return y_out\n",
    "\n",
    "tf.reset_default_graph()\n",
    "\n",
    "X = tf.placeholder(tf.float32, [None, 32, 32, 3])\n",
    "y = tf.placeholder(tf.int64, [None])\n",
    "is_training = tf.placeholder(tf.bool)\n",
    "\n",
    "y_out = my_model(X,y,is_training)\n",
    "total_loss = tf.nn.softmax_cross_entropy_with_logits(labels=tf.one_hot(y,10),logits=y_out)\n",
    "mean_loss = tf.reduce_mean(total_loss)\n",
    "\n",
    "#learning rate decay\n",
    "global_step = tf.Variable(0, trainable=False)\n",
    "initial_learning_rate=1e-2\n",
    "learning_rate = tf.train.exponential_decay(initial_learning_rate,\n",
    "                                           global_step=global_step,\n",
    "                                           decay_steps=50,decay_rate=0.9)\n",
    "\n",
    "optimizer=tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
    "\n",
    "# batch normalization in tensorflow requires this extra dependency\n",
    "extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n",
    "with tf.control_dependencies(extra_update_ops):\n",
    "    train_step = optimizer.minimize(mean_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training epoch1 \n",
      "Iteration 0: with minibatch training loss = 8.13 and accuracy of 0.062\n",
      "Iteration 500: with minibatch training loss = 1.75 and accuracy of 0.36\n",
      "Epoch 1, Overall loss = 1.83 and accuracy of 0.328\n"
     ]
    },
    {
     "data": {
      "image/png": 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4yNqXotVb09fz148X1XQYQYn6pFGZfww5nXdv68P6Jwfznxt78f0959HSrc7y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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6cc3219668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.52 and accuracy of 0.464\n",
      "\n",
      "\n",
      "Training epoch2 \n",
      "Iteration 0: with minibatch training loss = 1.63 and accuracy of 0.36\n",
      "Iteration 500: with minibatch training loss = 1.27 and accuracy of 0.5\n",
      "Epoch 1, Overall loss = 1.37 and accuracy of 0.505\n"
     ]
    },
    {
     "data": {
      "image/png": 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HwD4Ad5jDXwfwPgA7ALQA+FS+6ALU/TS8hAbXTlLMyqfgEVUiI6osjaKxLYHW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\n35KtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c4c21e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.23 and accuracy of 0.553\n",
      "\n",
      "\n",
      "Training epoch3 \n",
      "Iteration 0: with minibatch training loss = 1.31 and accuracy of 0.55\n",
      "Iteration 500: with minibatch training loss = 1.52 and accuracy of 0.47\n",
      "Epoch 1, Overall loss = 1.13 and accuracy of 0.597\n"
     ]
    },
    {
     "data": {
      "image/png": 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bcpsyVDXymTMD+E9gLGYTjIlG6GchEI0Q1uw5idk/WuyI1vPyHRYrPIWGLSie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\nptrXhI+pmOfBBZ5VVZhh3QErsNPEPHWmIvEwCK0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3fzncbgW+BywCXjWzqQDh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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb20e8f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 1.06 and accuracy of 0.619\n",
      "\n",
      "\n",
      "Training epoch4 \n",
      "Iteration 0: with minibatch training loss = 0.904 and accuracy of 0.67\n",
      "Iteration 500: with minibatch training loss = 0.773 and accuracy of 0.67\n",
      "Epoch 1, Overall loss = 0.972 and accuracy of 0.653\n"
     ]
    },
    {
     "data": {
      "image/png": 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AmzANQsi3CCHrCSHrCCEvEEI6EUKGEkKWEEK2EEJeIoQU5ZKGY40ZbD3odJXLphFR/0Qi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\nWi/pyjQsh/k7G3ge+HbatXezpI5MsnV2MXB76s4iX0T8L/Bl4LfAMxT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb24189e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.85 and accuracy of 0.713\n",
      "\n",
      "\n",
      "Training epoch5 \n",
      "Iteration 0: with minibatch training loss = 0.964 and accuracy of 0.66\n",
      "Iteration 500: with minibatch training loss = 0.722 and accuracy of 0.7\n",
      "Epoch 1, Overall loss = 0.858 and accuracy of 0.696\n"
     ]
    },
    {
     "data": {
      "image/png": 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ApYos1fF7X1mLOaYdwskY3GXtBZus/nDjrYhJDJJKvVRXqrajGP8qFIAmyZ8x\nEEK+SgjpTQw8RghZTgi5tCOI6yj4DYja+lY85hGlykPUgfMvmJcLm4wC7wA3bzra0lkc9tm8RyaR\n6G5ZGoUeVDbp+UY+C/1LJS+rCiyeI5WhvhIDO159sAnX/nGRcp9vWdrtfy7bjReX622fqZJWxbTX\nfpCNN892s87ybtWR+FusN5ik9dT7O3DHM0aiQt7uJiud8FQlSav3pYE5mshsDLKEkDI1j8zGIH1v\nNe1K6kVX53MGHYnhC5TSegCXAhgE4FZ0kX2a/eBlsONx599XaNdprSTM8acydrmuCzgW/CZQncA6\n2fjVTaKn23deyCXymQqfOrSweA6e+am8xkTa2tJGLiWxX20bg33sf/5vFZbu0Ns6U+VFZH33uKe/\nvL2NK+f8lIFSanUYK2dnAXZe6Nqf20e15HecB/9OyHX0kNKkKi+iSTL2WWCcrM6EpTmwj0lVSRJJ\nx4sc1Tk/xsFf11mG6CKNMuw2PgbgCUrpKtIVEoprQHdyEw20PLbUNjj2W7Y1HfIXToWgqwQ/mlUT\nXiqTtXavkj1Fl41BpTKwVBfedHohVOQzMx7Kyisu3VzbgF2Hm1FcxFwWs3YMi6I9UeXQmsriggcr\ncbipHdsfuMJFb9isn6f8cK7rGC9FeNm/fvb6BhcdXuUzWerqN9U74FIlCXXxv51Sm/+AyPgwPvZs\n0pKq/CbKtXvqcOUf3PYV9nxk7qpM2vOzNWYc9+mmp+pAI4b17e5ZR1Bkafg0ILlAhzEsI4S8AWAM\ngO8SQnoByMHkWDhgz9RPe+IVhS/m0hf3ytWPY9AqZsHPC1bFkCZ8/3W8cPtZWLu3Xjqp60oM0ok5\nIGQvol93iRv1SBxFXLjUfEYPf+ZUAMakz56pSnX27X+tdvxuTWWkqjmVu6qFEC91RrIyVVVNhXJe\nqqQMpS73Uzu+wVlWrEackGX6dj96GXxtDAoaVMd48Husd+NSwYgSA1+NVJXk07bort3cnsZFv16I\nK6YMwcQTenkT6QNRCk52Qg4NHcbwRQDTAWyjlDYTQvrDUCd1eehubhIk1a/1UDVeVMd12i2wdsJP\nyDc/sQStqSxKi92aRDcjk7djvUQRSwx+9+Wl4vBjUkWmKqk9k/W1MYhQbXqvclfNBfx96LsP+0u/\n2SxnixAWLv6qJGddSklSg1w7jYjK+MzKyer3boAfPwN7dLO+e8Ux2LsRcvX41C0yYqZiXFR1ECf5\nMIYgIyV2uljEAAAgAElEQVQfxm0d6NgYzgawiVJ6lBDyWQD3AAgW0legkPni7zjU5NpcJ8g7L66k\ng0Q+uybFHN1VVWhNZR2fPNLC26jWJec+YKWMQfgt9r3orsqT8eLyPY7c/SK68aokOFeQfvjUn+Qe\nZaqNeuwCWtVbaE1l8PPXNlq/vZ4zv2CxAiq5+/na8yscgXq8hGYZnQW3VVlZADgiSEu5GE7TFmMg\nivvz8kryrV4Ky/js4ZXkt4iTuquav9k98ZJjWCMyf11neSjpMIZHADQTQqYB+DaAHQD+lleqOgiy\nVdYFD1bilic+dJQLwuFFEVPbxkDdg8CrXd34gbs+Ml6vIKPD57fVfu4Cg5aNQewTO6WDu/yDczfh\nKol+mYHZVlKcxBBmsyAeqo16LHoD9tDf3t+Opz/YYV/vpUrimpSp1l5euReVm+xNmjJZiSpJUM2J\n9THcLmx9qpq8tFRJZqclEnLG4CXNUYG53fH0MrxfdUjaDi/FJYQANxmcKjx3uUyWYl9dKx6av8W1\nqGwzF1nJBHElIlRBGQsiqJI6AzqMIU2NO7gawO8ppb8HkJsSrQOx5YjaO0cWhAYAS6oPOwsGUSWZ\nn7pqKvu6YFOI7oA5ZWifALXq12t7ZYQfuLJtT/26KyMwXpFeL0cBtprbWNNgvci5MIb7Z6/Hb80g\nQdUICRoAKLrFeo0f3p2ZdYOoKuH34uBXxKxeJjWKzfhmaXWoXZzMWUzWKI4Ra3VNiKe7qsz4zD/v\nhrY05qzbh5sfXyKni/vhZcBlpVKZLH7+2gYcUgSGZilw1/Mr8Jt5m10Xt6UzZjv+c4WvV5LQZmdA\nx8bQQAj5LoDPATiPEJIEUJxfsqLBHU8vw5x1ct0w4A4KUiGYxOD8DCYx6I8C3Qm8RGJH8K7X+Vst\nMejTqrqvmrpWpDNZFHF6GH9x3qrU+duE50tnlj3U1I5DpmokbFI2APjLO9Vi1ZImg9XvNgLLr991\nuBntGbfbp6gq4VUbvPGZUueiyc/G4EWnyETmb6jF584eraybT9xHJQyIMTw/4zMbV+0KLsZfrnQO\ngH2vW/Y3Ysv+Ruw+0iJPuUKpS/3K6GlL21KQH4JIEp3lrqoza9wAoA1GPMM+AMMAPJhXqiIC4+Iq\n6OawD7I3iJi9MohXkqpkS3vG4RIL6K8kSgJs62nQoVblPPPBDkvNoUqiJ/PcUfXBs4t34pOC7l4/\niZ6cXq9tTGU16wb0+UFFtrgHRVConvOf364SysnHGz8hOiQGStGY4tQyPqokETzD83NtFe+B0UiI\nyl2V0ehuN4hhni/rNWGL95rKZKUMXW4TM1VJTGJIkJwdEQpBYvCdNUxm8CyAPoSQKwG0Ukq7hI3B\n7wHJjM8yBPJKEj6D7MfgsjGY7V7/5/cx8/43Hed0V+zdAuzeBni7q97z8lr84OW1ZvvmeWEFd9p9\n81x1ygx+DKt2HXW8mH4TteheKXav1/3Kuiyq/b5Vz2PjvoZA9bgmVQV9ospCdRv82M1QZxxDWZFb\nFWWVDaBKEg3V7rpEiSFr0SYj27b/uM9lsxRLqg+DUurrvMDT4bVfhljN4urDSndV1RauvI1BRguP\nrhDgppMS43oASwB8CsD1ABYTQq7LN2FRwG+lbxnsfCaHQF5JArNhg1eVUoGnRaV2kGVH1Z3PigNK\nDLr1ytJfqyQDlajP8CgXwdsuUSyzHDtGu3C0K07IJV6MQbYKjOjFy9frq2I44gpYdRv82DWMz/aY\nTwjndNqVnfeTNkS7B1soJJXGZ3Xw4ezVNbj+z+/jhQ93+UrjTrWTupx4rq4lhS3cbm12OarcE5up\nkooSJNLJvGAlBgDfBzCTUnozpfTzAM4A8AOdygkhfQkh/ySEbCSEbCCEnE0I6U8ImUcI2WJ+9svl\nBnwo8Dxrufj52hiCSAzOFS0bvA+/tcXnumCuabq68aCMwW8LR1ZGJjGoVt8yKWDKMNsoznvhyPzM\n//IOn/pBlBgCqJIk5HlJM0GQL+8R1dgUJQbVZORwaeW6Nkudk45fCgwRqpW5eA5Q2xgSKlWS+Slb\nS+01M6HuPNysIe1R7pu87OzVNWjycFjgIWuOHWKqpAQhvtKWda2KfI/n0lHQmTUSlNL93O9DmtcB\nwO8BzKGUngRgGoANAO4GMJ9SOgHAfPN3XqC7LaPvAAslMcCs2xglB30S2snGrae7qrYqKZi+0yvv\nPkNdS8oqt/uI7YEShDHw3jL85Oy3w5alqpMwJsBOlOd1LY9c3VUZ1u+tD1Q+10RrojFVNR4cOnnO\n+JzJUocOX4cxLKo6aO3z4LUgELtUfKZ8HIOMbK/sqgx/rKzCmxvcW2dSxz2paQIMNeZX/r4c95jq\nUT9kJLokxpCZxJBMEDszruKZ+KfdVktjHQWdCX4OIWQuIeQWQsgtAGYDeM3vIkJIbwDnA3gMACil\n7ZTSozDcXp8yiz0F4JowhOvA793zyqnOG1GDGJ/FlA2ZLMWRpnYs3X7Y6zLDXTWIxNCJqqSjzSn5\n6lu4uLa+FU+8Vy1dlfO2AP46GROReWmwI0EkBun+DyEYQ1NbGvWtKcexd7Yc1LqW+fCr3Bp5EpMJ\nomS2oipJNYnyfS/mKHIacp3XydxsP/OXxbjv1fWMUq5tb1US325Le8b24FEwBiu7qs/K+8f/We95\n3kGHpCH2DPd67pNiIytRJYk2hqIEsRaD/NhqTWUw5Udz8ca6ffCDg9ROUiX5uqtSSr9FCPkkgHNg\nzLWPUkpf0qh7LIADAJ4wg+OWAfgqgHJKaY1Zdw0hZLDsYkLI7QBuB4Dy8nJUVlZqNOnEoUNOV1Wx\njnXrN6DP0S1Yud8tSp523zz8+oLuGNA9gTpFumwZTe+//wEGlSXQ2Gh4o7Sn0vjSo29hc623h9TG\njZvQt97pabJ/f62jDf57Q4Oet8vSJYu1yjFsrapCJbV3vmppaYXIYj9YvBhb97vv5513nMFln354\nAarrs2jcW+Uq21hn201a2myPq6XLV7rK1tfXW/d+1HwWjY2NaGzM4L1FTq+m9tZm5VhZtWq169i2\n7TskJb1x5k/nojHlX06GOfMXomc3RS4IANXbq63v3RIUtbW1rvuprKzE7p1OCfTQIXmQ14pVq6zv\nixcvQVOT8U5s2VqF/sk2sGe7es0ax3XrN2zAgIatEFG9uwaVlUewmXN1ra3d7yizdWsVKjM7rd/v\nLlpkfT/5h3Os76n2NrzHnWP3ub/WoHH1Qb3tVflrd++2x1J7KmUd37TD+cCampqwyuwb3QVZY2Mj\nUkJAxOo1a5Cs3YA1O436GxsaUL3deDe379yFykqjb/Y2ZtHQmsYPX1yOmeXGtLutuhqVlXtc7bz7\n3nvW9/cWLUK/0uDb5jQ2NoaaMxl04hhAKf0XgH+FqPs0AHdSShcTQn6PAGojSumjAB4FgBkzZtCK\nioqAzQMv7F4G1NocuqKiApgz2/rdXFaOioopaFu3D1i+zHX9Nxe24MlbZ6J//23AYfeLd+as84A5\ncxzHzjrrLIzoX4ayFQuBxkZQkkDf/gMA4eUR0bN8JMZMHgrMs5PylZeXo6LiVItmvg9Y/X4479xZ\nwML5vuUYxowZi4oKO1r6w+ffhOGtbGPmGWfg6PpaYNNGx/Ezz54FvGV7T7UnSgC04JTJU4BlSx1l\nywcNwNpDRp8kEkUADOY8afIUYKkz8rx3796oqDgHAPDQ+veAo0fRo0cP9OyZxbSpZwALF1hl+/Wx\ny1ow+++USZNdz3nosOFAdTWCICxTAIBpM87EiP5lKJr/OtKSlCSjRo0Gthr2qJ7dSzBwcH9UVJzm\nGLcVFRVYkdoMbLPtVr379AMkzGHSJLvvT5sxAz2rVgKNDRgzdixaaqvBnu2kSZOAFcut606cOBEV\nM0c62gWACSOHoqJiKsqqDwOLDaY8YOBAoNZW64wbNw4V54+1rp15xpnAwkoXbWVlpTjzrLOs58fG\n9yu1K4Ea94TpBXbtwoZ1wI7tAIBkssg6vv29amCDLWHsasji5ElTgKVLoYvSsh7oXVoE1B21jk2a\nNBkVk05A1bvVwPr16N2nN4YN7wdUV2PI0GGoqJgMANi0rwF492306tEDo0efAGzbijFjxqCiYoLd\ngNlfs2bZ79GZZ52NoSEytlZWViLMnMmgZEWEkAZCSL3kr4EQoqNQ3Q1gN6WULVn/CYNR1BJChpht\nDAHgPWPmAD897jMfGKsaL3XCq6trlDrBu55379Mg6r6zlGJQr1JfWv/w1lYrC6gOdN0svVQrMvj5\npAPM+CxRyyi8UWSk8qqkFKe38HNXFY3eLlWSh41B1md/1dyAKSo0mLvmqVNo2ChWeLhks9RlY1AZ\nqdOC+oj/zneHS5WkGF4tZu4lnq6565y6ftHQq7Lj7Drcgg01tjsvqzOqdIR+Gpn7X9sgOaqGbMzb\nNi9mV7D7kh9vfFCfH/hWCs7GQCntRSntLfnrRSnt7VexGf+wixAy0Tx0EYD1AP4N4Gbz2M0AXsnx\nHpQQJ3SV54bXHEvd9iYLi7e5V2iiG2VUxk0ZXTrQGYg8XORK2qFU4eGj8D7JyDJa8hG5DhuDu+Ll\nO4+6bAtillAGL0YYlWtqLmgw9drK2BiOxkRCroPPUDdjUC1uHHYF3ispSwXjrL/xGQCazSyiXj3p\nF8fA40t/s1fsrFhUmWp9TAzYdkCdcFFVn0ibzRBs26LMqYUd4587T9N/PS2XXDpryOZ7z+c7ATxL\nCFkNI3X3z2Ds/nYJIWQLgEuQx93gVMEoIrwmDEqpcqDK9ky2/ezt32H9mqPwSgrKGN7cUIuvcZKQ\nrJWs4p7EyYkxijaJ3yH/gqQcXklyiYGtSr2S6AHeEa5RBbPlAltikJ/nSVT5+bemMi7GopYYso4y\ndoyAj7uqoq9Ytlav8See0XUJtiSGHPiCkxnYP6J48lKJwRqHxmcmS60+TyskBtnt8VIX9XguHYW8\nMgZK6UpK6QxK6VRK6TWU0iOU0kOU0osopRPMT293nRzg4u6Kcl6qJJknghduf3opVu466ni4YaUG\nr5WTbpVBorYBI5ju5ZV7QSnFws0HsPKAhPmBStt3SQwZNWNQJTVTMYb9DYZB0s14nW163W0hMIZU\nJounP9ghXVQATjVMkhBkqZGKhEddS8qV5ls1hlUpJPzdVeX0M4nBa6YNIjE4rjM/o1IlyfIq5YJM\nVhbgxj5tKUGqSmISg5YqiZc0wtObC7SMz10Vbtcy1arKe/UTZNG9ubbRseIGosvHw0P3ZQvKGBjS\nWerIWskjm5WvZMRAIZbpVMYYVExP9SzYhOS1gxvg/ayiYgzFSeIbb6FCKkvx98U7lef5bk2YNgbR\nz76uJaUvMWT4SYZyEpfTTiRq+1Qr1RYdVZLLxqA3/rMSiYGoHbh8EfW+BjK1shjQmqXUUp06d6oz\nJQbivtbdEN/mMSgxdDbESUI2L2SzFC1m+uciyazyysq9WMDltNdBaXHS0ZZfOgxdsEHy3taDOKBI\nDSwioCbJwj+W7lKeU0kMX3jyQ/dBAG0pt9uhil+pmGhzWxqpTBZHm80AK/O4OIF5SVlRMYaybuHX\nU+lM1vNld6iSFH7+hsQgMAah23qXFpnHnVICqy8jGp89Yg94tFrGZ+UthJYYvva84arMK1uCOk/w\ncEgMEQUEiIogS2LI2gsVazMpSZyIzkKNfxavr/WPe8gHdHIlXWumr6gL6JXU6XCrktyDI0OptbLt\nVRqNAFVSnHS0FRVjYLr9m/66WLvOsBLD919SR4NSCunMsL9BzqxkuZJUAV6q+2pJZfDlZ5djD4sp\nsV5IJZkuRGV8LuuWDH1tOusdyMhPJqqNbOpb0lLjM3/o6unDrPb4MvbmVN6qFhWNaWsC1O9LXemK\nTYL80AiaBNIBx/2Fr8aug7rUEKLkms3aEgPfppUGhHtIKm9HXsp7cO6mXMkOBZ1e/yWAqyilfYJ4\nJRUCVFGKPLKUoqnNeNG6F4d/4XmUFiUcE5ZfEjkVZKqwoIveCLci5ugIpvtsk/jrqxiWlypp3nrO\nQGd+uiQGGCtqmeThtXINYqTPZZykM95bMvER90UJIu3n+hZ3IEU6m3VMoux+RD23xRhcXknO+lSu\nnFZ2W+UdeEc+64AfGl5JEb3aBET33EAkBAa/Ix6b12WJBvkFkWocdJbBmYdOr9dSSoM5/BYKNLyS\nslmgsTWNHt2SkbnJlZoTRzduK8koYIip+oOGkGg3qbfpkMcxqCD1SlKMPHHPaQam22YQvZN4TPvx\nG1L7iCdjCNJPOXRpJpv1XL3+37Ld1neVxNDYlnYZm7PUqXZhatF/Lbfry2Sdabf5hbzuuLLTnuup\nwwB9GwMDP2ZLivSYsB3fwun1ufPRqZLEdp39kc5Q7phRpqktbaUSSSaI72qtAHwkPAPcriWEXAtg\nKSHkBULIjeyYebzg4YpjUKiSGtsy6FVarJysgqK0OIEspdZqJ6yhEgAWbrbtG0En5ECTXQCIwVF+\nkG2YpGJY7Yq+ahYYQ31rGnO3p1zqIRaAtajqEJ5d7PTm8dwmM0g+rBBv7rcuM8J5Uhn9KUolxKSz\nWVf/Z7IU3bhJlEkMq3fbqUcM4zP77u2uqoK4taoMYW0MDPxt66qS/PYPj2oRrnKBZ83vOdqClTuP\nOtp/pLLK2pdDZ5wVgvecl1L949z3ZgCXcr8pgBfzQlGEEB+CrL/nb6jFlv0N6FGSdG3bFxalxUlQ\nag7qtvASw8pdR/HiCjs1AKXBVj5h7Qt+oPCmY2DPEhzkjOOvralxlVFNeqq+EpnL4aZ2PLcRGDzM\nGTjfwLmBvrJyL246c5T1OypVUhhbBVvBp7PexmcHTUQuMaQyFAniVteUSFRJzjJOt0oHY4hQYtCN\nfFbVH0aV5BVjYHzPfbKlcG+8JLqrArDsYOwcP6YdG/koSCpoxkApvbUjCckHxI6XqSm+anpCnDqy\nL9rTPqmxNdG9OOmQGMIan90psIOt1PPEF0CptwG1WAhSqK13G6VVTGuhwgNM1dzv5zv3uWjgsp7u\nOuxMNOg1oQeRrgJqRgDYWW7TWX3WToi8rfZ01uWtw483AI59tBkyWepQffD9IY1L8bDT5MMrCTAm\nUYdXUkCJQWyJBahGITHU1Ln3j7eyKEsbMI9xQ8tpY5CjEBiDjlfSU4SQvtzvfoSQx/NLVn7gZQTu\nXpyMbIVdWpwEheGd5NeuF8SU2UFtDHmTGKj3CrNIEr1WWuy8FxVt62vkDm+6LzYfOFZb73yRvWgO\n0lVBdeaA3SfpjH56daKQGAxVknH8/BMHATDGBb8alTG6LKUWoxHdVaUR1pIFjRZjcNEbTGJIRCQx\nAO6AyKhh1S+zX1Jjnw5+rCcSxHHR3qMtjsWMcV3nMwYd/8yp5j4KAABK6RFCyKl5pCkyiO+Gl67f\nMApF026RmeOGDWqZV44OxBfKeLGDMIZQzfpC1E+LKJIYa4oTCbTC7od8STP8S+Y2gkakSspB4xhk\nkjR2OHMfT2XsSf3c8QPw9uYDyFLq6HcZc3Zs7alhYxAN/qwOwGeiFepSORTIkM44U9DoSgwqXp2l\nFAlEIzHIYOXskjyotzbux1sb92PMwB7WsQThovYBzHrgLYweUOa4Ll/51YJAawc3fvtNQkh/dJGI\naXEwpDxUOoQQ7RX2SSf08jxv7JRli/YyrxwdiPp2qog4VkEWfv+LT04JRYuDDurtcikLFBQnqqDS\njO5dezF/L6YahDGEWdFlshRFCeIb4MbD2MjGXbY9bUsMrB/TWYrxg3taZWT3wzsNZF0pMdztezGG\nQLmSAkx0og1GN8AtYy6a/va+0+GA1ZSvVbhofJah+qCdrI9n3uza7YcElWcXYQy/BrCIEHIfIeQn\nABYBeDC/ZOUHXkbgIAKD34PLmnpk5mon88rRgZh8bOO+eqkhVwXZ5HvpKSeEooWHEWinPi/Tb4tq\nsaDSTBTGQ68JKgijalTkOfJCJktRlCRIZ/XtREQpMdiTJ2MAR5tTAAEmlhuLFhlz/toLK7HPVK/x\nHkqMPhF3POPeo8TySvK4B3ESDjLRiTYYbYmBUjv4UUJLvqZamfHZC3zGXNXiqrPSYPDQ2cHtb4SQ\npQA+AmPuvJZS6r2nXoHCazWZCCAx+EW+GmI6RUlxbhKDqMu+4dEPAl0vm3yjiO6moJ56dtH4bBxz\nvuC9SotzpiMoPI3PAThVGJuRITEkfAPceKgkhlTGdlfl6U4QYv2WjWUnI3BuByprR2bvsdVRwSZ7\nXRhMz/6tHcdgMl4RVl15mmyPNLfjpRW7A0iB7vxKIrqExEAIeZpSup5S+jCl9A+U0vWEkKc7grhc\n4bYx+EgMmnPDI5893fM823Q9V6+kXOIfAPfk8N7dH5Gu5oOCUu970lEl3X7+2MBt5oqMz8Ign0hb\nEoN3gBsPQuSRz+3pLH4zbzMAkTHYTFk2SfIQk+gF4XV+EqPLKylA5UYQHqdK0vVKEmwsIi35mmof\nnLsJX39hFXYd0ds3WscrKegCMB/Q6fVJ/A9CSBKA98xYIHDZGDwGKCFEO0rYb6s9lpNGd7WjQmNb\ncJUFYDM40cYQlTGaUm8pSEeVVFqcRJ/u+lJD2MjVX86xtx/1khi8ghuvmjYUf/JZDPjBkhh8ciU5\naCLylfzavfVcGafEwJ65nwSk45WkvNbH5TYXG0P1wSa0tNtjS9crSWQoDFlKkc5k82ZjCAqHKqkw\nSJJCqVcghHwXwPcAdDeT5rGR1g5zL+auBi8VQIL4T5y/vWEa+pZ1820nk6UABXqU5MYYRJFSNwVx\nkhCkKXXdT1Sr4iylVpZNGaQSQ45cKexL9MfKKut72JQYA3uWoLx3STgCTKQ547MuCOQ2Bv4+eKoJ\nsfvZ71lnqb8qSQU+HkJed3gbwy1PODP06koMVCHFfP7xJVizuw63nTdGm4Yw0H23koTkTXqJEl5b\ne/6cUtoLwINc8rxelNIBlNLvdiCNkSFXG8MnTh2OCycO9m2HJSvrVVos1beHhTjwPz1zhLRcQjE5\nRKUtoQhuN5Gt/IJMRl6MSBdeE5TXBipFSX1pUt121lAlZfTTmiQIka62VfYdAtvG4NdGllLUtVH0\nKjHTcwdhDD4BjuLMl4v7ZZAAN9k9L9txBO2ZrGuvkKgh275WhkQCWLT1IIDo8jflA769Tin9rhnU\ndgYh5Hz21xHE5QqXjcFjMksQEtnEybySEgTopyFhhMXwft0x7+vuR8FWvyJjiEpiuPnxJaj02KNC\nNhHkqlZbvvOofyEfeE2WXn0TRb9ZEkMAVRIhRDrh8LYSfkJPENsd0m8uzmYpapuyGDvI8LEPYmPw\nUyX9+e1tWLvHztGku7WnDCUeNrF7rjjZykElqsZEbK5tDE2DDnRvsfpgE1aZ+auW7ziSR4pyg47x\n+TYAbwOYC+DH5ueP8ktWNHClxPDg6iS6+DZkTK8kQkheGQMhBGUlbm0gW/yK81m+DawMMumgpDh3\no3eu8JqgvFRJEdjrkclQFCUThvFZ2ytJzmTFPRbs8rbE4CeNpbMU+5upFXwVVJXkV/7uF1dz5cNH\nBHo5S0wa2gcj+xvBYZR609TUXhgSQ9UBO6bhw+1dmDEA+CqAmQB2UEovBHAqgGBbmhUIVJk7ASYx\nRDNx/mfVXrSmsiAE6Ncjf26ZCSLf6kNlgMxXJDSPsQN7YCwX6cmQq8QQBbyNz+7OOWN0fwDRZKk1\nvGaINCXG0D6l0msIkau/+GM8k0gkbBuDn15/X10r2rPAmIFGUBwvTYnqz0tOKXe178dH+EVIKgdV\nkteYNeyC7H697VBidt6ooSsVHVBsZlVo0GEMrZTSVgAghJRQSjcCmJhfsqKB+D57Gf50jM88PnfW\nKP/2QfLqr59MyKUA5gHkUiV1AGe4dNIJUgZbCBKDd3ZV9zG2so+i35ifvSyJnqr+BCHSCYd3FXYY\nojmJwY8xsGjcMRJVkuj2KToOqDyAePBX5OKX77VYSyaI9dxUNgbWHy3tGW0PpzAohNiDKKHTU7vN\nJHovA5hHCHkFwN78khUNgrirBglwA4D7rpksPT6wp+29Qog82CsqqOwi7EXuDFVSMiE3cufzpdRF\nUK8kVjxXjyrAWNknFe6qqseSIERKcysXSZ9xqJLs+AW/FT1zhWaqGH5SFQM4xXHDG5/PHT9QWj8/\noeeyta3XmOXT2Kj2CGHvX1N7OpLnqEJU28YWCnSMz5+glB6llP4IwA8APAbgmnwTlg94qZKC5Epi\nEFU1t8wa7XBrTBAgGdXuPxIQBWNQSgwdY2KQ7mVbGtG2qbkgqFeSlY8oodqdN1jbSWI6JmhOIkRh\nY+C96/jzBASXTx4CADhpiHc+LwbGBHiaygQ3a3GMZTh12BVThyhpt2nMhTF4n3MyBndfsVxLze2Z\nvOxmyHCsSQxa+REIIacBOBeGI9p7lNJoNi7oYHh7JblfCD8kCMBrLglxMgsCgjwKDGZ+J5kqiVjn\nneXzzxko1ZcYOvpVYpPopKG9sW6vM92DVwqJJCE4ZWhu25ynzbTYsglExXZUXkmOejNOieGqaUNx\n6SnlqJPsCy0DW0TwK96yYue0IC6A+L2jVfYX/mgqnYuNwdtbjFedyRhuNy77gJeHU6441hiDjlfS\nDwE8BWAAgIEAniCE3JNvwqKAOKZ+8qqd4kk2aT543TTcddEE7fpdcQJwSh1BJQY+k+Twft7R1YDx\nwkpVSQqJoSOckrJUvsorBOPzvPW1AIDTRvZznZNNcHyiuuJkAnd9ZHzoto19BoihhhHOqVVJ/jEA\nPONgK+LS4qS2hMMWEXwz3f1USZydxMs+wpDLnudeY5aP9Farkux3ym/8r/jBJXjoxnA7Chx3jAHA\njQBmUkrvpZTeC+AsADfll6xoECQDaCIBDOpVgm9ccqJ2/b6J1wgJpNfkg3mmjejrUZJVL1d/qaJf\no5YYrj11mOsYBZWK7OJGPZ0J2XOT8e8MxxgA5MRZb5k1GskEsdKl6EC0Mdx4xkhcM32olEZWnkFX\nbUUjwDEAACAASURBVMIWI39fvNM61r3YR5VkRvYDalfe6LySvGwMNkNX5W/ij/n1Sc/SIpwzbkAo\nOo9HxrAdAO9PVwKgSl60sNGDWwmJE3YY/aO4yhTdCxMESAbQJal24PqpwtCtShXO9PniZKd6yc4Z\nH+5lGDe4J3581STnQSqnqRAkBgZZP8iOscW4nbHUPndCb7mLqQyz7zoX00b0NVRJQtTw6189T7m6\nF20MnzljpGtBkxbGG3+tDsQcVoA7AZ84znmvJKVEzNsYcpAYvNZVyQSxzqu8knjG6dcnQR1QHO0c\nL4yBEPIHQshDANoArCOEPEkIeQLAWgD5DSOMCOIzbk5lcMkp5bhq2lD8/FrnhjVhDLNi/VlKXcZA\n8aX6x3+drfTk4HWkPJNQeTapvJLY6lzX+HzjGSPlJ3wgMyizwD4R+fTOCgpZ5lHVxjaA3Y/MFtC7\ntAjnnyh/hjKw65nEwJ5z/x7dcPIQte2CwCkxFCXd44mPguZP8c/+akHK4CHrC3FzHJkqyUr7rWNj\nyCHyWVRriXRZqiQVY1DklZKNR4LwUvXx5JW0FMAyAC/BSKa3AEAlgO8DeD3vlOUBlAK9S4vx0I2n\nYvQAZxBWmAFRL2zY0prKCJGo7gnnjDH9la6b/KKDp0eWTpjVL40ZMFfn4hmVVBR269GSooSUMXVQ\ngHVo6EoMDOwZ8kWC2I54IzYfx8AYhOq5JAQJtChBXDp9pSqJK/O1i9XqUZnE4NpUSdKmdU+K1QZ/\nS7nYGD5x6nBce5pbZQk43y9V/iZeWuH75/9VuO1FhAAkpMYzl7QfhQilVxKl9KlcKyeEbAfQAMN5\nJ00pnWFuDfoCgNEw1FTXU0rzEhsuGyjdu7FcMs6TUejfs1mnax4hch94VVM8Tfx1qtz6fhKD7lA9\n1BQuGrOkKOFKbkepnNH46dW/+9GT8PPXN2Jk/zL0LCmSbhITFWR6cdkExyblpCUxGDACyfTbY2Mi\nwbySzK5gPXLppHL8eeE213XimOQDukQaGV2ya5PEULnItB2y8VksLFzENu99ZS2mmzYwJWPgWFMu\njKFbUQLfumwiXly+x90GsVVJbHMsEc7+sY+7VcnhXNYZCiWtd1TwUiX9w/xcQwhZLf4FaONCSul0\nSukM8/fdAOZTSicAmG/+7jAww5oo3kaxys1QqoxE1QE/uPgFqWxVBzhTYvDNMIlBd7AeagznfVwi\nVSXJ29UhZek9F+P1r54XipYgkEsM7nKWS6ZEYlBJcTKwsZYkxNhSUzj/7ctOwrcvdycTEJsoTiZc\n7SptDFwxQtQLH6naz5VKxfl71e46PGXuraxyrnDEMfispv32UFfRzscxPPp2FXYebnaVcXp1ccxS\ncY9hY31q6lrDXeiB4f26o3cEOy6GgVerXzU/r4y4zasBVJjfn4KhnvpOxG0AkE/2jDGIBrEo8uG4\nbAwSVZL39Rw9vMSgcgnkUmIQQqzZl0kMugu1sD763ZLE1ckUcl2vn22OEDtqXCcgamifUuzVeBm/\n/7GTcemkclzwYKV1TFeVJKpL2CRKKQ20srQkD1NiEPsnmSAY0EOWbNEtMYjt9uSSKKpUSQmmgpI8\nBNnQcu/Prb5XnXQhXvug3Hf1JAzr1x1feHKpsoyqBUOCMs6+t/UQ1u6Rb0Vq1eNQBQoSg/nZUYkm\nVSgpSlgp7ZMJorWgyge8VEk15ueOHOqnAN4ghFAAf6aUPgqgnKu7hhAi3eCAEHI7gNsBoLy8HJWV\nlYEb37fPrSLZu3sHKitrsOaA0z6we/duVFbu96zPj4aamn1obrFfgu3btqFVyN1VWVmJgwflExq/\nBWLNXjvryIb1a6XlN27YiLJDW4wf3Ag6tH8fAKChocFBM/v+leklqG+neHq9ISn0PrIZv63ojq9X\nOrcnvGFiN7ywSS1NrF+3DkfbnCN39+7dkJksqqqcjmyVlZVIp9Pc+W2ozO4CANQ3uld+PKYNSuLr\npydxyxzj98fHFeM/VfKArtbabajO7nQc27lju6vcoUMHXceamg06Nm7cgMq6LaiuNvoinU5jz55d\nVrlZQ4twQg+CF7fIaVi7egWadyRx8EArGpuy1go6lUpZz2TjLve1+2qcmWeWLH4fe/c4y51ess++\nr53G2AaAVi6obMkH79suVgIWLlzoOpap2+f4XbNnt/RaAFi7Wq48OHLE1g4fqVOrBbdu2YIj3eWT\n8cieFJWVlTjaJqd9yeLFaObusxjuDKr8Qi3Vbo/l6m2C6o4abeXiWhsFvjS5GC9saseBForWlhak\n0jTU3NfY2BjqOgZfOYUQci2AXwAYDIOxEgCUUqqzzDyHUrrXnPznEUI2+l5hwmQijwLAjBkzaEVF\nhe6lFv6zfxUgDOpJEyeg4pwx6LPzCH69bJF1fNSoEaioONn4MWe2tD4XDUK5QYPLUdV4CGg1GNK4\ncePQmsoCVZsddTyzYylwoNZVPz8kR44YDuzcDgCYPm0asGyJq/zkSafg/JPLgTfnGPprc8IZN3ok\n5u/chh49eqKi4nyLTkY/u4un7zaOX3jhhQCAKac1oD1N8bGH3gEA3H39BXjhvnnSvgCAqVOmoKa+\nFeAY19Chw4wVz65djrJjxo4Ftmxy9ENR5VzAZA7jx41DhbkPdMmHC4AmNXMYOGAAKipmWvd196fO\nw38eeEta9tTp03HuhIGOZzV+3Fhg6yZHufLBg4DaffjaxRPwuzcNZlvavTvQ3IwpkyahYuoQbEAV\nsHkjioqKMGrkSGC7MbmcM2Ucxg3qiRe3LHO1//tPT8fV0w3j6cv7VqCm/SgSqVYgk0VRUbH1TGqW\n7ATWrXFcO3zYMGCXvS4795xzsCG7zWoXAC676EL8V/sG/HnhNowePRoVFYahubk9Dbw516Bv1iwU\nLapEuyTDaEVFBYYvfgu7zT2Lr5k+FL+4fjr++b3XrDIjRowAdlTL+/fUacDSxa7j/fv3Aw4fMvux\nB1Df4DjfrSiB9nQWJ5000dgqVxjf4wf3xD2nUVRUVBgZSRe86Wrj7LPPQn1LGlhkjNdRg/vi4I4j\nuGHGCPQoKcLj7zlpLi0pAdqMRdmJE8YDm+yA12QygYqKCsMe8kbn+dZMnzoF82o24UBLA3r0KEND\nutU972igsrIy1HUMOorSXwK4ilLah9vJTUv3QCnda37uh+HddAaAWkLIEAAwP72X6RGDuVieKkS/\nqkTIpfdcrF13llKXaB/ESMkvVnj1kUpa543PvLGPeT0FNYiNH9zLoVbyE6uTkhxCFPJo3azPSmxg\nL1uVEtTDwytBn8wUIFPvMTURf0e8Csgow9WrGVXOmIJBC3GkrObdk2WPShad77VtakJBX0KiguLx\npfPGWt8vnXSCW//uoS7SsbWkJNIKU+kyw7gI/pCKdD4lBgBs3mcwn6umD8X0ke4AUS/js21j6FxV\nUjJpv9MJ0nmqJJ1pq5ZSuiFoxYSQHoSQXuw7gEthxED8G8DNZrGbAbwStG59GtzH+Ajc8ybYvuiq\nsd8rgPEnS6kjpN6wMXh38Q0T5Rv5JByMQW18k4Exv1w9Jfxc9xKSlByUyoN9slQdqPeTqyfhGm4C\nDerFUuzRxzLbkeyYzIjPilmMwTpOHH7wqn0xZO3yxme+l2RprEXDcIIo7se8f9Xj9tudUOX1xl+v\nAuubHkK8gZ9XEnsPVYZx/hD/bH7/6ekOuviFV4OZMdao0/s+RGZnT8bu6zoS/PNl46UzoMMYlhJC\nXiCE3EgIuZb9aVxXDuBdQsgqAEsAzKaUzgHwAIBLCCFbAFxi/s4LZH3aLWkPYD5nDj+Qf3ndVOu7\n10vxP5caYjtb/WSyFJOH9cHU4X2sMrIVHlsp/vK6qfjoGPl+DfwAUZFAZG8P7BV0rmPKzyAvzS8E\nKmUMFBSfVexhceXUoY578csPJJ714r2y1a70mGXEt49Zq9qEswyl1JkskehFzhcliTLZm+xZiVX2\nLi2W0s68iHijvTtnl5o+Rz4hyXm/6GP+UwZZEj27b+VMi38f+b51SGCKfjcYtffzUEkM+czAqoOi\nhL1hWKIQjc8cegNohrHiZ6AAXvS6iFK6DcA0yfFDAC4KQGOk4PMR3XXRBGzd34jZa2ocK7brZ4zA\nt/9pGNUShODn105B3+7uCfy/PzIBV08fhmU7juBrL6x0OX0kCJG+aSyffo9u6u4Xs7TKkCAGE7hh\nxghcP3M4PvnI+wBsN9JcVxt+YvWUYX1cLoJeEgMAvPKVc1y7WIlzSlCJIXC6dOlq0vjkJ4bu5vOR\nMQ1+MlWlJpHR6dj9jOsm2ZPi7+v3n56ORMId+QzYeb/SqihoH1USL/3IinmNIkaPF2OQeZkN6FmC\n7Yeacbip3bAxCHCq7YQ2TZWcqj+I5BqxTlk6m6AgiD5DsFNTIJckOwK+jIFSemtHEJIPyB42zxiS\nCYJJw3pj9poapTtlgninjBjRvwzr9hqbe1uRrFz7sheyxTQCslz4p47siyunDsV9juyv9nWqwVGc\nTIAQgl9wEg4AlFo2BiXZWvB6WZbeczH6lBVL0oLIJwLWN47kgJS146zEL++MuOL2mpRkZzxtDAQY\n0b87dh1uQXdFahGxjgQhWjmxkgmCQ036MSM8mWzSl0oMZtspRwpuJ31eq36nnYBYbbPH4LXAUEkM\nfJfJNur57kdPwm1/W4ozxvQ3HDQ8IC6MepYUWWnF5QkRFVIIzxh8YjV0kCDG/u5RgrfbJRMk53c4\nLLwC3L5tfv6BEPKQ+NdxJEYLVR4Y1eDXES3tOozfrKaihDzArcV8ERhjeOnL5+CL545xlOGvU7n1\n9ymTq6GisjF4vSyq2IrbzhsjjZ/wIkVsxk+V5He93zmZ3cdi5iB48xsXYP1PLkOZKTEwCYYfC/z9\nE0Jw3viB+OK5Y/Bf59uGXFUbUkg6iG+PMUu+3e9cfhIAW3pJOdI/wPHdaxzL8gbxyfr6dpfbwQA+\nwaCXxOC+t0lD+2DlDy/F1OF9Fat7t5qUHeNVtXK1oFrFJNJtt6ckX4mg6b8G9yrxLZNMEEs1SgjR\n3tQpanjZGJjBmeVMEv8KHlIbQ5HIGNRldcGMd+zltQOjEtIJlKWRKNNUJakSdPWRqLeACG0MHstM\n2Qv599vOxLhBPaWbywRJMhY0G6dOcOLG+y63y0tGPS8xlBQlUdatyNKDt5jPi2/FwRhgTKQ/uPIU\nDPJ4+cU+oIrvNk3297TgIQUAF5w4yKDFHH9pyd4MgCnReDIG954FbAF1xwXjcNt5Y2SXmXXDRZcI\nmZcZ7wQin8TBnXee++NNp+HJW2diYM8SJVORUUNgpIqvmDgoMolBxCM3naYsP2pAmW+dSc4+oohJ\n7BAoGQOl9D/m51Oyv44jMVqIro3Waj+CnPFshW6nUpBPoJYqyWPHOKfEYNP26ZkjrO8yuwdg57rJ\nXWJQn/PaZ0LqrhqAlmtPGw5APdm4jM8aLzWfCdbLC4Zvkl3TbD4v/lxSsDHo0OKlIpONv4RDYsi6\njrGVJfPKUmUx9VUlSYzPjNncdOZIZUoWwJ64vO5b5q7qZFzuaxR+FQCAXqXFqJhoxMXKGJ4qfTYh\nBL+5YTqevPUMCWNQku/Ao587HX1NSV12y177qOhoH3hjPLu3zpAadHZwm0EIeYkQsjxkrqROg5+N\nwSjjVAOFARtkbPLzlRjSThuDtE6Jl85FJw3Gj6+29z9QSQxFAj1h4TWQRRdO/odsAuR95RnK+xh7\nGogv8QPXTsHqH13qKq+Cd1oG9zmvFNv8WnNkf2OFxyQHvj/4fEJ+kxyDuHJ2xDGoLwPgjqkw2jK+\nWxKDQtLyVSVJ7oXZHfxSutj7MoiqGfu3OAz/9f/OFuiTTOISryQZJWpVkrssf8iL3j99Vr3q51Of\ny7omqL1Ldj0rZ3vBaVwYMXS8kp4F8C0AawCET5NYIHDbGIzPXCZRaxeprLOuZEI+6C2JoVjd/fyA\nZ5NCIkEcG96Im7YwsKHVr0ytG84VbOJwruyYSsPZlyVFCSsPEo9nvngmFlUddAQFAsZ99U4mlCul\nII9KbmNwHrz9/LGcHcE+/uULx2FYv+64YsoQ1znnBG0f92JSIsN0qJIk98SXT3NjwG6XMQZTYlCs\nbni9tQxiNlWA3/LTu7PZMw+SE0wMivOT+LzOqvJe+cVGuLySPCngy/Eretn53JDkuBrrpiylSORc\nczDoxDEcoJT+m1JaTSndwf7yTlkE0LMx5C6usdWGqEM2JAZ3F7MkWV6bkPCShrjx+hO3zMSdHvsP\nTxneB9fPGI4nbp2peQd6OI2LJvWaB0S1iOrFP6FPqaU2kkF8In/67Om+NOpAnOy+97GTLcbAqxqL\nkwlcd/pwazLm78IRFMbdn9fKPKhRnTcmpyXGZ/bVimNQSAx+6aRFewlg2x38otCH9jWkvts9jO4i\ngu5F7hlgpzjn5XkGuHdW9GpjwuCeXCXWP9c1fu+bzhQjlRj8L4scOozhXkLIX0MEuBUkVMbnKFRJ\nVFQlEXlKjPuumYx+ZcUuWviXi18ZWoZHczBfeNJgfPNSd5pmhj7di/HL66ZhSB+3f3hYLP/BJXjs\nZnvgy1JIiPQyBFhMeqLEY9/oV+88V3q8d6lb3cYku/LeJfjdDUYkbVObv0MAP4PxDF/0AFLBJTH4\nxDHwe12w1NT8ROiSGDwmcf66K6YOcZyTGZ9/cvUkjBpQhiF9nVuYPnvbmbjjgnHW716lxdj+wBUu\nl26vhZY4B0sz23I94sU4ZJJQghD0l2SrdaiSiMgY1G289JVzLOZAOHrEay6cONhTZNDRSiQJJ5FE\npBIOAx1V0q0ATgJQDFuV5BvgVggIZmPIxfhsfFpeSZzeVbZy+dxZo/A5SRTw9z52MvYebcGrq2sc\nA5cZHr0MvvlG/x7djMRsAoqL+NWy8SlOgF8QXHF1wT+Sd759IbYeUO8oO3lYH9exv3/pTIznV3sm\nmGR31tgBuOZUI5K20Uyn0KPEQ71n3h+FuMp2T9YyuFVJnI1BMv4Ys/rWZRMtY6tclWR8egUGOsii\nwIUTB6Gl/jAAIZjS/FoxcTAWfstOfPyrT03DCb1Lcc74gTjSLI/FeOfbF6IllcH/e2aZ5/vkYgya\n+cR01IKsvgFlbtUlf32QPd97lhThhD6l2LK/0WHID/o26swwfGp1O817wIYigA5jmEYpneJfrPAg\nT4mhimMI346176wVEGR8JhPENw+NCD4GguH8CYMwsGe3QOI6D929C/wgm/SunDoUX39hFQD7ReEn\nwO0PXJFzu4ARSMgYA6u9exHQ4uZV6FtWjKPNKcwaJ9+XOSs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zpQHcPLCbbz+6JbXdt6y1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\nWfw/tXo6kkFrnfQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c3c223160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.788 and accuracy of 0.73\n",
      "\n",
      "\n",
      "Training epoch6 \n",
      "Iteration 0: with minibatch training loss = 0.681 and accuracy of 0.8\n",
      "Iteration 500: with minibatch training loss = 1.06 and accuracy of 0.64\n",
      "Epoch 1, Overall loss = 0.765 and accuracy of 0.731\n"
     ]
    },
    {
     "data": {
      "image/png": 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/W4CP/HqWcVppFVTPGXUhKjLHLfv8XTqDVuy8f2TI23Zf+fu7eGL+ZqzaaaWG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\nLRcHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c3c583f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.799 and accuracy of 0.704\n",
      "\n",
      "\n",
      "Training epoch7 \n",
      "Iteration 0: with minibatch training loss = 0.687 and accuracy of 0.69\n",
      "Iteration 500: with minibatch training loss = 0.537 and accuracy of 0.84\n",
      "Epoch 1, Overall loss = 0.692 and accuracy of 0.756\n"
     ]
    },
    {
     "data": {
      "image/png": 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YCIT5G+sw5O6JWLylHpMXb88cuiSCvz43MP95xprAb6NzP9VKDC6hjS3tWtVE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\nanZ44U5yxVCAcwoYqc0GN+3AvWM6m3XTAb6PwaxMRx86hRWfWMyiY2b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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb34960b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.707 and accuracy of 0.752\n",
      "\n",
      "\n",
      "Training epoch8 \n",
      "Iteration 0: with minibatch training loss = 0.537 and accuracy of 0.86\n",
      "Iteration 500: with minibatch training loss = 0.691 and accuracy of 0.78\n",
      "Epoch 1, Overall loss = 0.621 and accuracy of 0.783\n"
     ]
    },
    {
     "data": {
      "image/png": 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LXFRNho7OK4kHP5BFl8mspKGoE810gf7zgm2YMKQ6uKBRriJ9SgyuIABd5HPw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\nx3GcCtwwOI7jOBX8PzLH03Cc5MhJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb28d9a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.655 and accuracy of 0.773\n",
      "\n",
      "\n",
      "Training epoch9 \n",
      "Iteration 0: with minibatch training loss = 0.515 and accuracy of 0.86\n",
      "Iteration 500: with minibatch training loss = 0.576 and accuracy of 0.73\n",
      "Epoch 1, Overall loss = 0.563 and accuracy of 0.804\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pb0VAVXgUQpX0UQCPZf5+DMClBaChIEjloEp6bMk2hwhtipZUGh9+4HW8vvFQ4GdtCMTe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\n/g+kzgMpb0CwkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb24fc208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.628 and accuracy of 0.783\n",
      "\n",
      "\n",
      "Training epoch10 \n",
      "Iteration 0: with minibatch training loss = 0.44 and accuracy of 0.83\n",
      "Iteration 500: with minibatch training loss = 0.43 and accuracy of 0.83\n",
      "Epoch 1, Overall loss = 0.501 and accuracy of 0.826\n"
     ]
    },
    {
     "data": {
      "image/png": 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33iy11UpaB6xy7oCV7WjfN3nyZDS8OhvAUe+yALbP3wQsfTv//aSRJ6H+opGe\n77R+dxPw6qx8WaxaiZqaGs/67135GrDfqYvv378/LrroLOCFGZ7PmTjxXDS+sRlo3ICTR4500yT1\nBX/+MrYWWLXKdd2rvIyeS2cDhw7i1FPHovv+1RjddwSwYhmGDBmC+vrTrEIvzQDg3JzJz7n44nqk\nvDYVAi0j/drbLnf2Oedg/NDe+e/nnDMBpw3ppVxbtry+EVjmzC587sRJOLF/Nzy3bAe+9PDC/PUh\nQ4ehvn6sg56+/foDu5yHEkVZ+3zXPQ3oMIYUZwo29kLPNjEZwLVEdBWAGgA9YUkQvYmowpYahgLY\nFpLmRNHcmkF1RQppn51q0aqgIu/3OsUMEALHAvSgfrspuU6VSuY7Ty9X0OX9TJk+1Qla8u3FquxE\nVfTHH3wDAPDMl6bYz082VxJDOFWP13dVvRw69XsehRpwLwnPCmMviMvVkteiViUF61t0qYim7vMr\n621jeGODM/BUpZIql/MqdBb4GUT0HBHdTEQ3A5gOINDJnDF2O2NsKGNsBIAPAniZMfZhADMBvNcu\ndhOAJyNRnhDG3fUcvv6Pxb6nmBXbeV63+9XqtZDJ9xTW+2Bvn4NH27FP4QXhWqA19Z58oOcYcMuj\ni/yPw1QYn92G2OLaWdv4rOOVFCWOQadcaLuKaGNQl35k3ka86zezfcsEUUfkM0592is2xuCx4QEK\nOno/xOlJF+Y4XWUApWLMAer2LYd0GICe8flWWCqd8QDOAHAfY+wbRTzzGwBuIaK1sGwOfyqirkjY\ndbgF33nKO932k4u24UibdybRYrpu7rq9+TOOo8IxaSRiuCTgN+D5T2d/7wWc/b0XHL+NmDYdt/1z\nieOa7iQTi/37ra1Yu6vJs6wqJYb8lGLXGE63eOaGynCp83rhcyUV567KGMPMVbt8Axq96v/W40vz\nxmbvxd2fLvG4VHlB8x1bMS1sYauRPf30GUNwuTCSrF9KDFlKV0sXgeR0CLQO6mGM/Ysxdgtj7GuM\nscfDPoQx1sAYu8b+vJ4xNpExdjJj7H2Msei5GyLi7qeX48+vNarozH8+4hc5XETn3Xj/6/lnxzGH\nXF5J/Lp9edehFjzx1lbnPfaPuru7KIwhCDqBeMUuMjnGkM0xx5kbkSOfo8Qx6BYUv9p3/WPBFnz8\nwTfwD8ldWiRDp/88j48MeGmxfd7afAAjpk3HvPV7feuU6YsC/lwv+rhXmwyv1NYiDh5tx4hpTluH\njut4mLjF6GGgAAAgAElEQVQeP68kd4p11bPKgzN42hiI6DDUtBMAxhjrmRhVScOj7cWJpjqspnB7\naTvP4bIobWTlALdPP7wQizcfwJRR/YX7wz4vPF0iDepB5JYZ3BNQ+Bxhxcnm3MeGZvIBWKJqTl5U\nvFVQumDQS4nhJTFstb2JtkheRSKteSbneYwmU6eBRjDTErOrvmoHv72yZjcmndRPWeeLy3fivlfW\n45wRfQJq1oNX28lutxzZHIPop6C6XyXB6tmB9NV9qnFaiDF0dpQ6LXwwPR0BT4mBMdaDMdZT8dej\nUzMFQOmtwBjD34QjKa/4hbc7X6k7T2QGMilygNt+24bQJByyE5ax+e4QxSR4OpXZq1ghiZ7wHLkG\n4esfZ69HWOSYmzGoFngd46KfxHD1r1/F5T+fJVWi1x5eealIFv1siO0lktTSnnXVpWKMhef405Vy\n2GCs/wTCvTPXYvQd/3GV//wjb2J+477I54XL8DrPuk+dOumC3D+6Y5xLGs8s2YYR06Zj8z53evIw\nqiSVjYCPOXnZUdVTDgn0gGP0zOcKBWNoWL0b3450zGd0RB0D4uLmMtJJKpq6KmsbJarGvJ777SeX\nKq/77arEiRAqV5L9f7+YLM3FFwoXFnuoEPyQzbnfNaPI5eOa+MJnLjn6SQzLth3C2l1NONRS8GFn\n0NyNyt/tCzy1Ov99/e4mLN58wLng2YV3HmzFmDtn4ME5jY66ssxHagkx9rICs3pg9gbfskUvbKqI\neAHda9RKjqxkkNaWcu2Cj79pqVv/MrfRs0wB3pUrHR48JLuomX47AsckY1C5oja3+h9byRjDv9/c\ngvZsDp98aEHoZx5u0Usj7Pl8YTBmNHbpvHytzRiOtgsSg8dNf5mrPjnLb7A6mZREA/N+Hp8kzy/3\nNsSLEydKgoVsjrkmqiottJvuwoUxd1q+/jouku8Rkp/p2BgOHmnHE4uc3tr8HlnXfunPZuG6e+c4\njc/2f56U7eklzrpyueDx4YUcK7SDaJcJeqe4Fjav0+O8Fvx2qX906fjtzLX28yzc/6rF+PgBR+Jv\n+e+Kqv/46nqMmDYdhxTpwnk7yud7q1qzXIzPOnEMXQZ7m1qRY0A6wnFQTy3ehlseW4wt+92RpDq4\n8pevYs60SyPdCzgHo+ghIw9SOQdRtyqri5tbBYkhpCrJb7fsxxjCPs89AYubJSpVkjKJnvRknbQG\nKqwRdNiyjaE1k0V1hcWkH1uwGT1rKvF/b2zC4s0HHHUEvXKQV9LnhAAqS2JwF5q/YR92H/b3+RDd\nbfOvThTYJ3I77W9us4IWQ8LrzBIvW5P8XL/NiOtZOWc7zd+wDx+6f55nXSoKHprbCMB9mBQgJBZ0\nPdddT9kbn7sizvn+iwCAGyee4PrNL4inqTWDHQdbAFiurlGwVZGawDUEfMaEuMA5J4GkSuJX7ctc\nYmhqDZYYvOCnXxd/09ml+QXgydd+MmMVHl2wGY33XK1DpguWjt19DZDsTD4qLI7wcQzOaptbC4yB\nuwOfMsidvZ4/m9PnXpSY8jNgta2YudTLxnDwaDuus4/Q9KRffLZCSvGC/LzP/XUhdh4KP2e8hpIX\ng3bZGEIM8m89sdQRG7FJsjMEnYEiXlPRt353M847qZ/LXVWVDLDTSAxEdAOsjKgDYY29Tu+VpLIx\n+AkRp931XP5zqTpOHIwZn1164aQ36wduY3jddjUEQqmXAfj78PsZn/1USSoa5IXu0QWF/DxRzsnO\nMubaYaolBomOiBKDow44F8nm1gz6Sjtn1Sv5eWbJ34PWvpyCMXL4umODSwzWzXkducYzRckUAOZt\nCHfMbJC7qlcAWFQbAwD8XXA6AYCjkkeijiSbZwyK35ZusxNHSv2t6oNysTHoSAw/AfAuxtiKwJKd\nBCobg+6yE2fK7DAQn5r1WYzdjMHqYtF+EHbwxSkxcOhIDCKi2Bhyih2zKp2zTlrl8HEMTlVSU4AN\nK3+f/d/rXA1nZl0n5Ch2L1WSFh2s0A6iW2xQfU8tdto5/CKofZ8vfc8HbnpKDPIZ0dHnaYu0YOcY\nw7N2KnIVbUHP5XYH2cZwROESXy4Sg47xeWdXYgoAXMFt63d7R+jKKBVHFwecuCuR6eEqMT7Aeivc\n+0KrknzUKL7GZ58pFMbOEXWSq1RJGYW+V4fu0BKDS5WkyRjyu3N1/nRHbIdEuMx8sjm9WAolHcKj\nRXdVz+o8OLdKOudYs/Mw1u46rPztr3M3YnuTW1L1YtBy/xSzwMoSw8srd+Hzwqlz6vgDpqQDsMbc\n+t1NTg88uBkQULqNpwy/ALcb7I8LiOhRAE/AOrAHAMAY+3fCtHUYLv/5LPzvh8/WKhtnv4VZ8BzG\n56z3YpySJAaetrp/92rsabK7T7qnPZvD88u8vYP8dstO0VldTrzqF/Hs9ZSobX60Pas4Pauw+/V6\nrkzb4Zb2QtuFgFjPYW3GYP2X+1EoofwIAK0Z50KazUUPxVQtflF2/xWpFNqzarUVjxVS2ZBW7TyM\nH+4HbnlHMF1A9DgGFeTg1r1NUj4xn7GrGqtbDxzFpT+b5bp+pN09JjqDKuldwucjAKYK3xmALsMY\nrM7UU1YkydH9d9gF+LqrSiI3LyqK2vLg+8OsdfipdMKXCD8bg8ikvJpGd6wzxpQqFysbrF4dIg4e\nbXe9a2HSe6uS5O8Tf/CSaxcZBMbgOMHtaIBOP38fd9P08qDx5gtKxhB1oRFVSRw/f8F7jHghbL+J\n5VsVTeYluS3afACnHl8wexazvsp97Y7odyOvdlPQt2ybOwane3WFh41Bn84k4ckYGGMf70hCSg3d\nAVwsRx8/tBeWRAjWcqiS/ALc7P8Fdz9bxM16M5P9PufrAv7ZLDM+rrOF7wrRW1EXY8BvXlrjup5j\n0WwMB4+0uyYan/SiBNAuLagybWGZQr4eUcpTzHiVQd27Dd3f5b5vy7h9+aOO11xI+4RX2WKyrbZl\nrbxmOvXd/u+38ci86HY0ETITD+oToDDPVNK1iuQeNRXKzUK5uKsG2hiI6CEi6i1870NEDyRLVvmi\n6CRhUba+0nP9UvMWVDXWxH5ltRWo0+5YwJ33d6/290Fw6W8VBue3Nu3HAUmH6jvGQywkUSf5waPt\nrh1cq2KRb8vKhstIj3PWIbEX3QWSl5IjnzmcZ3H411WUjYEV70wAxK8a8atv6dbCoVeqYrozT8Vg\nRajPUbD+6wRCAhZj6OxeSeMZY/koHMbYfiI6K0GaSgLdQVNsvnSdzJTZHEOKvD1n/Ay+/I5H5m3C\nX+dtzCcdy0gSg0hHD480AxzyLuillbscv039xSys3tmEqgrnPkOpp4Z6weNwnNhWJJSqpIx74soL\nQRj1tFd/Wgtr4Ttvw8cW+B+R6XZXZZ6/B5GZY9EZg3W/flmvomElhqB5qHD9V6KYBbY1G8AYfGwM\nOmdFAECPmko07nXnZVImPWT+B2slAR2vpBQR5VMmElFfdMHAON2GT+qQHgDI2ANy5Defxdf/sdjx\nm7h4ORd5SZVkv8f0t7fj2bcLwU6yW6k4gAMZgzRR9jUX1DB/mLUeq3c2uWjkzwH0XVMZA6or3EMy\nF3FiHDjqViWpsuYG7RD94LUQMMguxjnkcsxx1oXqjQpJ9Kxf5fxHDnfVQIkh+njNMRYqMM2rzeLW\nmetuzHgxMbmgLiku1aKsSvJ5ni4j7FZdgbZMziXRqjLAlkKI0GEMPwPwGhF9j4i+C+A1AP+TLFnl\ni2JPqPJbdMTF+99vOs9QEBcvP3WC1vLJnFGXtVXhJAbx++PSWQ8ivNIaAOoFi4EpJYaoTd6Wybna\nW5UPqjWTQ3s2h20HjqK5NYPFWw64ynjhyUXq95fjGLI5t8pK5HV3vWusdR+sMzT+tbBwDoMYnyC+\nTZDnzaZ9R7DtYLRI/fYsw/wQwWkdtXjpOn8wZqXjGHPnDPxvwzr7mt69LtWiq26fOawp0lSlU8q6\nVSiFeknnBLe/AHgPgJ0AdgO4wb5W9mCMYethvY7SHTRB47J/92rP35paM1ix/ZDn737xAq2ZXH4w\nOT2MLNrnrtur7b2zu6nVwWiWb/OmCXAzQ219uU+bev1SXekekoyxaAFuzB35LIPIYiB3PL4UF9zz\nMm56YD5uts+HDgJjDLdKp93lf4NbYpC9hkR8fPKJqK1MgzGGTz60AMuFcSKGAoSxMXz6L+GTPXLs\naWr1pbdU0A00zDGWz1vED6rS3WC4jc+yjUEVl2KVadNUJVVV8Hij4PKlsDroGJ8fZowtZ4z9ljH2\nG8bYciJ6uCOIKxZ/fX0jvjXnaP7kKT/on1IWvNB44eYH5vumNMjk3KIlR2smixp70ZS9kmYs3YEb\n738df523Sevg9qt/PRv3/Gdl/vvvZ63zLS/ndNE/+a1AI4dvHIOnKknrcS6oAtxkVFek0JbN4eVV\nlt1kwcb92vX7DQWVjcFly5DA4wTkBHeO874d4ye5JUOVDC4pOMeH//jVlhggRo+Hu1dWN8q3rdh+\nCOPueg5v7Mi4ymRzOa3NWSWXGDRIKkuJAcA48QsRpQGckww58YK7hW5UGHlk6C4+QYui32QNWnQy\nOea5I2rN5PIJ8WRD8ub91vtt3NOsbUX/15tbggvZEN9ZlcraC34DWqlegio1sUdhzecHTarqijTa\nMjnfCF2/+nvVqg+OAZzuntkcQ2vGueDIr+qV1tohJTiekBx2R0h8FxWy6sYPujaGHGN5SauQPlzv\nGbK3kDyGNuxuBgC8utUdc5PJsrxk74eKVEpZtwplZWMgotvt4z3HE9EhIjpsf98F4MkOozAG6ERB\nht0FR/3dD5ksc6iJ2jI5fOvxt7HrcIvFGGz9u2NyRLExIFzuH7FsezanL877lFMnIlN70eQYixTI\nkMsF92tVRQpbDxzF9gi6+D/N3oDBvWuVvzHm7BodiSFlH6cpM4ysg8F4x43EiY6UGILaRUQ4NaYz\nQ62uhCXHrcjPrLSl2n1H3S7g7bmcyztPBa5K0kEpGINfgNuPAPyIiH7EGLu9A2mKDToHznPoimtB\n5fY1t2FB4z5MGNFXqz4RmZxz0Z2zdg8embcJOw9ZNgFumHUm0SsspkThI0216BIklEwoicH6rzuu\nVWkDrHqYlopMdV9QtwadS+CHHwnqOBkMUuxJjrmNz/I7kXp8ObO0Rj9XIwzkvD5JojWTgzsBuRph\njM/2pryQ80nzGbIqSbb98WNyxchsJpTVkRi4Kkln3SlLVRJj7HY7qG0iEV3E/zqCuGIR5DMvQrfx\ndfLyv/f3c7XqUgVBifX3rLX49py1VpAaT6EtxzHwb0QUaQENgrhLbc/kilIl+Z3H4IWo02Lz/iN4\nozFc2ue4IEsM2RwLPA+ZpP9iXRxH2qKfqxEGPDUJH3NJotUeUy3t2eA4hhBOIvkgwbwqSe9eWZUk\nj/ftB62zVSrTwJ/nbEBrJpvvi/ZsLr/o+6HcbQw65zF8CsBXAAwFsAjAeQDmAoh+HFkHge+e56zd\ng3efNcS3rGbAonZkYxRkcszh7sb1kFy0VUsMBRCcp4jFhTuFs7C/8/QyPCkdR+mFlTsO4cJR/X1T\nCOgg6sTYeagV331meXDBBCCf4JZRSAwyyD4hTTbAiv192N6tEqkZ5kn9u2H9nubIdHM0tWRQW5lW\npqiPG63tWXz24YV4ccVOTBjex7esrsQgjhl+i7ZXkiQxyP3GbZbbmhi+8/Ry7DvSnmc+mRxDbVVw\nm1Wk9b2SSpE/SSdQ7SsAzgXwOmPsEiIaA+DuZMmKB3x+PbNke3DaB83FRzeyMQosG4NTbSOC2xi8\nDup5btmOSFlAw0CXKQDAD59diT51VR72BP1nqvTu5Q5ZYnh93V5MOtGpXnQZnz0We3Hx4IyhrjKt\nTvsQUzsdbs2gW3U6lP4/KnKM4cUVO/Of/RAmwK0Q2BZOYpCLyW67sv3l0NH20KqkMHEMpfBX1fFK\namGMtQAAEVUzxlYCOCVZsuJCYZas3qnO+86ha5iKU2JQuauKqiRZhK1VqJJEPboqxL7UWLFd3e5h\nxnpE27MWklKVWAtT4fv8xn1YutU/eaLXCWniNa7iSafU5y/HtcNvasmgW3VFh6Ri8M0WbOO1dXvw\n4JwNoVJiFNylLRVP1MzIsjeZEoIqqaqiMKbGHq8+6DKvStJ4n7K0MQDYYifRewLAC0T0JAD9bWMJ\nIY7poDGhO+DCnv0bBrJXkrz7V0oMSNYIGQcc6q4QDgEcSU6Mn77vjETqZYpeCZLmiEjZl06Jod2u\nX92GSnffCGhqzaBbVQWS0iSJTC3jc74Ix+qdTbj76eW+KeC9nrG3qQ2jvvUf/PHVDb7lrz1jMC4/\ndZDrepBtKMcKvZbJMVSlC402dZy7PqCgStKZu2VpY2CMvdv++B0imgmgF4AZiVKVAIIkAt3GVx3g\nHRfkOAbx1CigIDE4dj4anjelhLxORaE1ydfrVl2BqnQqlC+9DmSJAXCrIeU1N0V2inHZXVVhYwBT\nLypxMYYjbVl0q04nJjHIsTEcQX0dRWLgNoO5AYGuFSnKB5GKCIoAF9OsZHPMYXz2MkRzVZKOI0cp\nprdWMjwiOhvAFFg0zmGMdZwvWxEQdztbDxz1LavtlRTChz8VYbvltzvp163apkHwn0ayUkzcYPn/\nIYzPuWgH9eggRbZbY7QjFzzxwvKd+MMSp4TgWgRcL6WOYxBvOxQkMejoADTRrboiMRWely0tSN2j\n71YefqedThFqUm7VopYqSYCTMbhb8AfvPi0/Z3WWk7JUJRHRtwE8BKAfgP4AHiSiO5ImLA6Irpt7\n5OP5JOjqH3UXYZ0dqKqmFp9BOLCnxRhEafrbTy7D8u3hD/7pUAgv+vr6vRgxbToWbdJPVOc1L+JY\ntAiEdAJc51cvrYE8VA4cdR6ItFaye1lkuGM2RGmXB+IxpmatcUkMgKW6TIohi0xS9MQLksizOaYV\npc40ot5lVKRTypQsYXNGiQFu1RVp128fnjTc5+hWN8oq8lnAjQDOZYzdxRi7C5a76oeTJSsehBnU\nuptuXVVS1ARkqrTQHIN6uiUGAHjO57zmcoDYtK+ts8T5l4UzHYLvVwe4EQFfuGRkUbQRIZJkFwSV\nF9w6yZW4WfKXJ1gqHC4VcIieONtsyddLYohT9VNdkUpOlSSlC+EQpYefXuyOKs8yphUnkFOo8oJg\nqZLcEkNYz6xaoQ45Cjo/1Ox21VEllaXEAKARQI3wvRqAf9a1Tgh9ryRNiUFjMKme2eKjSupdVwWg\n+NTfHQmvZSVMSg6vogTgpgtGhCXJVUdH+OoDwLrd/jEmRJY78AHpqFVxH8DHB/O0MRRPJ0d1RTox\nVVI2q2YG4nkE/WsVCRVzLG+49YNOniyZ56VTpJQYwqJW8HSTXVe5RMf7ScvGUILp7mljIKLfwNqY\ntAJYRkQv2N+vADC7Y8grDmEGte5iqysxRFYl+UgMXOURZlFNCjwTqA6KzQLqNcGJFGklbFSmSS/m\nhBCrKom3i+qdg6RIr3eR37+qImUxhQS9kvhzklIlZTyMzzr3VVek4O98rheBLCct9JIYwqLGV2Ig\n+9nW/7/N3xRYX7l5JfFk7gsBPC5cb0iMmpgRRgzWHZvaNgYNiUFl1/BlDCl98TNpVKQ0F94Y4BXg\nRvDeIac16SNQrKqk6oqUr9TnBy8y5IWhMkXI5NQ74jglhqqKVKyMRoSX+igI/1y4BUM8EheKaM8G\nSwwpIkeZdFrtlRQWfqok3py8n37XEKx8KSuJgTH2UEcSkgTCjGl9ryT3pFelIdDxZFAtXKoziTm4\n3npvc+mdwqwFQ2PhjWFd8TqAiMh7h1yZSqEFwQs0xSwxVKWjMwavjcxeyXEinSI0t2XxhCIKPc6F\nvLoilaBXUqGN7nxiaah7g46iBaycXkGSqjyGK1Mpl7E4CmqrCszAU2II0bBlZXwmosfs/28T0RL5\nr+NI7Bjw3fuJ/bv5llMt5qcqoht1JAaVC22rj8QwrG8d6qrSRWUEjQu65xdYQVvFwev+FLwnWFpD\nDw3Eb2OoimFhkfGJh5wnylX4qEiKfZePnT88/7kqQePzA7Mb8593hDz7QSdavS2bC9QCqGwMcUgM\nVekCfdUuGwN/dhhtRnmpkr5i/7+mIwhJAmEyjXIviQtG9sOGkEnIVKqIqDlm5GMFRaRThBP7d8Oy\ngKM4OwJhFqBix/XUX7yCGycOc1230oyr6QjDuOL0/efGyyiv7LVWyO3nJxUUKzGIBw9VVyTnrvrA\nnA2R7+0WkPcM0EuBIY/hihTFIjGIx9N62xj0UVaMgTG23f6/0atMVwIfQzqucDJUm9OoBuKdh/13\nT1HoSwJJuHj6Q/08r4WrQnO1j1uVlGcMEbpflww/plfsq4jtlqTxuRjIrsDpFLnsbm0ZDYlB+h6X\njUH0RJIZDd/IhGHgpTAp6gS43UBEa4jooHCSW+CWlYhqiGg+ES0momVEdLd9/UQimmfX+SgRVcXx\nIiqE4bS5nHUUYJQdl+qeD973OmYs3RG6ri37/SO0O8q1Mgi6VNz3ynpXGuO44Ndfuu1EiJfJ8R2i\n+M6nDVEnUgOABz9+Ll659RKbFj06/N6tWIlBdAW1bAzlMd5E1FU5GYOKUbZldW0MznqqY/BK8pcY\nrP+63XTH1adiWN9gY3vc0GGPPwFwLWOsF2OsJ2OsB2PMe6QX0ArgUsbYGQDOBHAlEZ0H4McAfsEY\nGwVgP4BPRiU+CGG8d6wzYgnyhtxLn/n7j5yd/+y1sNzznxXaz+eh85sCMqSmQxivPnvRSdrPL3d4\neSV5NYMqFYFXvbEanxV+8Pd/bIJn+b51VTihXx0AfY8iP2GoWB4n7nYtr6Ti6ksC3audc1LFGNo1\nJAZ58FSk1JHPXs/wgtyGIlIhJYbRg3rEot4KCx3GsJMxpr/C2WAWeLRKpf3HYB3w80/7+kMArg9b\nty50c7cD9olPKXJ12Ccmn6gsL+q241hYetRYut2gQ1b4oqAzsErv1Bof/jbP7e9tuasW2mFgj+r8\nZ33JiuI1PitUfX5qLZF+XYOkbn1R4JIYylCXVCepklTGeF13VWc93nEMtSHSs1drRD7rNmtS7sJB\n0Emit4CIHoWVdjvvDsMY+3fQjUSUhhUHcTKAe2FFTB9gjPHzCbcAUB6tRkSfAfAZABg0aBAaGho0\nSHViy1Z9753GjZuAXA5bNm92XD++bauy/PJlBRe7nTu2K8scPepUC/HsmSq0twe7oDY0NOCQfawg\naTC9TZuCg2eiItPeHlwocTC8+uor+W/vGMbwsH1gW+tRvbMpFr31Jo7E6P57pMmdt2re3Nc8y7/5\n5gLsXWstJEeP6NHc4vNu+/b5ZxANwvp1a/Of16xcgaMdePazjKamJqhkwp1bneM6l3WPxZVr1uLo\nTv99by7jvG/dmjVguwv3/KK+Fj9+owU7mhnSTF8dumZl4dTABfNed/zW2tqKhoYGrNiWkW9TYsmS\nxchsDS8xNDU1RVozOXQYQ08ARwBMFa4xAIGMgTGWBXCmfZ7D4wBOVRXzuPc+APcBwIQJE1h9fb0G\nqU48s3sxsGWLVtmhQ4eictsmnDhiOLChMDmuunQK/q9xPt6WDlk5Y/zpwJtWDOCwoUOALW4bfTtV\nAihMrHSKkPMIuqquqsLhNv9JWF9fjz+tmwfs3YOKihQyAf7yw4YNAxoL3h/a0cAaqKqqAjSYWZJI\npVK4+OKLgBesLPCnjB4NLLcYdq+ePbClKdh765xzzsETm5cCh+NJRDiofz8s37vbca3+4guBl59T\nlp947rl5d+e6hQ3AkWCPuB7du2F7szq9xoD+/YFd0XNnjRtzCrD8bQDA2WeejodWLgKgt4jFje7d\nuwNwt8fok0cCa1bmv9fVVONwm3MTOPSEETh9WG9g4Rvy7XnIY3jsqadgwoi+wGuzAACTL7gAPVfM\nw47mJvTqXosDrXqM++wzxwNvWc+tv2gKMPP5/G/V1dWor6/H4cXbgCVvBdZ11pln4vyR/bSeK6Kh\noQFR1kwOnfMYPh659kIdB4ioAVYCvt5EVGFLDUOR4KE/YU5sasvmkCJy6VRTHoeniCKel6fQPmkn\nSq4gfOE3TZGRqz2iiJgVqRTas/EYglW69I6GrEoSm0TbXRXhjc/vnzAUjy1QbzhU7eJHi6jG0qUi\n7aNKKlYtViF51OTPfygjyO2pUq1ZcQxhcyU5bQxElG/r2hBGaVGdKNPKKdJXJWk/Nlb45Uq6jTH2\nEyFnkgOMsS/7VUxEAwC020yhFsDlsAzPMwG8F8D/AbgJwJNF0O+LMDaG/UfaUV2Zci8SpHY97FNX\ncKaqrCi+97QXBXtE6dg1ZLrDGNCCEBdjOGlAN1w8egAenNMY+l4/47NfEJijDlK7G/vBLw2WKsDN\nz8VY7BJdZu/Xj8XqpCslG0M5whV/oOhALeMzZAbjtDGkqNDWYY6AFftHppUzK91+6ni3cPu5Pr9x\ng/MCWHYC+S8IxwOYaUdJvwHgBcbYMwC+AeAWIloL64yHP0WkPRBhYgka9zTjuF41rgU35cEYTuhb\nl/9cGUOElPYOgksMEQZMljHcJES2FoO44ile/no9Lh49INK9ckqMdiGoUN9dNbzx+fheNZ6/qRZT\nv/pJQ/KU4ZddNO44hue+elFxFSYAt8SgYAwaEoOMqoqUgzFYEoNVdxjjc4WfxGCTpNtNZScxMMae\ntv8/FKVixtgSAGcprq8HMDFKnWERRpXUuKcZk07q51pwVSkduldXoHddIUK0I4POOOOKMmCOtGVx\n17vGoaoihfsDzr8Ngsr7Jiqier4QnAuhaD8J464adpf95ctGYezgnvjDK+uxeLPzwKGwklTawRj0\n6EhyvDm9ktI45bgeiT0rKmRVmqo99OIYnN8rpLTbKSr0Z22l1mGX+XoKtHqpknTHZ/lJDAAAIppA\nRI8T0ZudLVdSmDiGw60ZDO5d41okUmSlyRDRt1uVo8N08sMDyG8TRvSrQw/J5S5scFOKCM9++ULc\n+pySvh0AACAASURBVI5TPMuqFqlUirRSCgShMkY1Q9ShL6fEEFOd++nhZYSVGKoqUrjq9ONdTAFQ\nH9DjBx1blev5CTKGzqBKknfhqv5r1Yl8lm6rrEg5+oCI8iqkMBKDw24kPYQzK/Hy0D7eAWylclfV\n6flHADwI4D0A3iX8lT3CipLH9apxBbgRCLe/c4zjmrzghp2ox/eqxcv/Xe98jkf/y4OeSzREwNjB\nPXH16ccr7/t8/Uh8+sJCgNu1ZwzG968/DUA8tgY5OVgpIL+FmJ+qUjtXUrzR5N2qQjIGoRl1Nxja\nG5EIkFVJAPDXT05ylStlBL7bxqA4jrM9gipJqoeoYHSuC2F8rkgTvnf9aXjw4+e6fuMkiQt+/+7V\nrnIcpWpmndm9mzH2FGNsA2NsI/9LnLIYEDZf0eBetS4OTeQeePIA0lZb2P8ZGAb0qMZYISurVw3y\nswqPsj54LRK3XTnGscv59Y1n4SPnDbfviSEfTJwSQ8TBL0+a9mwuz/T8Fq6ThAy6BGdQ4w1nKcNq\nlLhszEDXtW5SVO5xPb3tEUA0iSGsKqn+FH0bjhzgBgBTRvV3levXrcoxfqNC1YZBkMe8ahNwtD2r\nkStJqkdx2hpnDFUVKVw6ZmD+eF1f+lIpfPS84bjkFPe78e4WnyyqpWWUs8RwFxH9kYhutPMm3UBE\nNyROWQz47rWnhSp/XC+3KknVL/KiGFWtIu4WvXSJrshJexLwSesnrXgnmCt+sMnMsBj1Rlz5eNoy\nuTxD8NtVTzqpoBoUJYaLRw/AxzyOCj1zWG/Xtd9+6GzXNVFNN3V4BeZMu9SXZj/GMKJfnVzcLpfc\nYiHSEMT841AnHt+7wDh/92F3e6ogM33VJuBIWyb0yYHymElRQYVUmU7hgZvPxRVjBwXX4zu/uPNI\n4QrPeqAsXcYSw8dh5zpCQY3UKVJxn+AxsUSI+r2BPardqhtFz8gTM6xXEh+vOi6n8uTk9wyw0z/4\n7f6LTTAXhq7qIrJSRh388n2ixOCfNsJZB2+ndModx8JxoWLXrNI7i26NFalgjyeRTHlc/eGj6hxL\nfhJDscZKv8yg7rJ6z/rABCtl+o0TT3D9Jr5Lz1rvBVKEvPCq2uOojyqJ0yE3lUuVhILEwF3SdXbw\n/l5ohbq9niuinCWGMxhjExhjNzHGPm7/fSJxyjoIontadWXa7ZWkuEfuLN04BnmYek3iD55bOHvA\npUpKyYzBZxB6XI9DYvjMRSMd3+M4Kzcs5MHbls3l+0/X199yV+XXvSeiV1+98LWLcL4ggYjGZ51m\nFp8nM3kv3/kkvJIG9qhGioBBgurLTzIh0qdjwog+aLznanz/+tNcB2H5JZzzQjqVckhTqq5pactC\ncdgiAGCqveuXb5Pfh6hw4BO3Heks1DrtUu65knR64nUiGps4JSWC2ImVih2jqmPkHYH2RGWOf5L3\nQqGYmAxO9gxJSYzBT1rxlhi873n4k8GexL/8wJk4Z3gfvHLrJRg/tBeAcJGhMood+nyRaMuwgsSg\n6esvqpJSRJ4T1muRHzWoh+Nwm24BjOFHN5yOV2+7JP9dlBpVxk+O/t2FgMoEVEnPffUiLLprqtQ2\nfpsOCm0TSacIE4b3Uf4GqL2gHv/8Ba5rFSnCjK9e5Js9eNXOwzjSpo7a9notuV1TRDh01Mqn1Kdb\nVf5aEHwlhjwNen1YKh8PncdOAbCIiFbZrqpvdxZ3VR3IPseyekfVf1EZg5xaQ3yWI52Db9pe638/\ne6D67bKi2Bh6+ug7Ofj7n9CvDsP7WTtAnQNOPBe0iOsc3xB+9fLRACyJgdOmsluMHNANd1x9qmNy\ni1KClRJFTYzuzk32g5fRp64Sw4TgSIfE4KPGFD1XdA8hEhGk0urTrQo9aypDqfV0x73XBkiuQzWW\nzzqhj+ta2o5Q7mvPAS+V0d1PL1deL5y77D+PiYADRyzG0Ntm/jpSoI5ErtvMZRvHAMu2MApWEj1u\nX+gU7qo6EAdtRdqdEkPVMXxgfb7eUqfo+q7nxy+PfhR3ZxDpKHyWByv3tOCqm3SK0HjP1bjuzMGu\n53kNKr9Fwm+nzW8T7+e7XM4g/OCls45sfLbbYuKJfQFYumy/SNULRw3Apy6Ud5mUf4e0Iu06h99c\n57f0k+JbVPe4TvQSbQyyPUmogC9QQDRVUo2mmka3L4iAKk0VqrghcHkCCXXonjsge555xSt5eSV6\nSwxuryQuKQyxbZE65myd88bFcabKxaYq15EIHC2ii2pnc1fVQaVjEfZeGETwbrztyjFY9f0rQ+vX\n+UDw2kn5RU5m7eheefcd5kwIv8XfbzealiYkUNjlXTx6AP76yUm+KRTuvnacNo064BLD4N61aLzn\nakwZ1T9P/7vPGoJPX3ii8j458R43nKd8jM9+OzfO8L93/WmOZU/VkrKR3hH57NqUFD7vF9JfR8nN\npXsyWdBm9/0ThmJQz2rcfe24EBKDIEVJt1Qq4iaC67PVhfb/sEdfejE/tyoJ+NZVp+K3HzoLZ9uS\nC2dCftPNT72bNz5r2xj0ysWN0kcplRjiQphOuU9wU+FIa0F3WV2R1vbykUVeHbUFH/yfmmItcjwx\nYDHuoTqLvwr8mSLj4qqTtkwOU0b190yh8N3rxuE95wxV/hZ1U6TSIHD6ayrTGD/U7WIqP49Q2KlW\npMiTAejQSFI5VVPKmwg/d1Xxt1ZH8J53/032SNGsG8UcpLr4zEUjMe+bl2PquOP0czs5GkJW35Dy\ns299drm0/Xwvt1SubhUhnmOtinwWQWRlCbhmfEEaz9gWbb/29LcxcDVW4ZqcBUFE2UoMXR0OtU0q\npdURTa1Oo5YuV89rknj0oygxiDRJEkPjPVfjjmss+z/fsciDOMymyVeV5McYKgoqF/lam1/K0YBn\nev0S1K6qdy4Ykv0Yr/BsKmTUrK1K53+rqkjhq5ePwkV2gj9dSVJco1SLrNuZoPBZ9krykgL9FuQP\nnDsMb3zrcteip80YAn4X2y5K0j+/xVh839OH9PKsj0sgfKwO6FGNy08txBdcdfpxSvq+ceWY/Pna\nKlp0Nlt8/vlpCfRsDIUyt145BrdcMVpdzkgMpYHYiaqjPVU40uY80yBIYuAHscgbm7S0QOWvi9kZ\npV0U15vKgz5MMI/u+QCu+/ISgyD+pwsSgwzR3dJfvHY+kxsVg9pVpULg9zB4Mxanu2ph0aypSBdi\nGojw1ctH5+NcdJm/KBWGlRjEuIBuVWlPZu+nSiKivMea33O9EDT+ndlg1WWH9Hbm/hH7UW6TSkli\n52We/tKU/PXJJzuloHzqed7XDPhfITiOz42M5K96xtBeOK5XTcEzKCDyWYWMrcr1Y7R+mY9584lF\nuldX4MuXjVLXZSSGjoGc8kAnilJGsyQxqO5552nH5T9/9zqnbl3prir87pQYJONzTj0wwxzM5mdj\n8JscopE2f61CjzHoBP0AwK8+eCae+uJkAMAXLjnZ8x7AQ5VkV5bNMW+1iIMhF/T+6ZQz2A0oTGCd\nCUpwMisdxiDukjnjvWLsILw27TLPNouS5l07IV7Aa4okqVyUiYApJzuDAR2J6eA931ICUxfxyKfO\nw/eEOcRfv2BjYMqdteu0QpL+S9CZ+1xiiJoSJk+C5oJvGEMH4PJTB+H9QvAY4O4gnY6YdFJfx3dZ\n5B85oBt+/v4zhTrV9eh4Dcm7ez4wZTtBmBTjft4fvjYGezKovKZUqiTR1ZDf01eh9xVx3ZlDMLRP\nHRrvuRpTxx6nLMMTB/qpkrI55t3uwsogfhbjGAo7O64TJsz/1mW+KS4YNCQGlx678Jm35YAe1ehV\nV4letZWY8dULce+HzsYzwg46bBI9xvSNz0HDX5wfX7pslCsP06iB3V33+Lmrit/5PAoSfmXmnc05\n1/ocs+rNSGMyr99X6Pl1wW18IrMTN4G60JVAS2V8Lj7/cicCY8w16OR2D9o1vPz1izFYEpVVoqNz\n8jp/52ofkaGIrnXidZkeXk5eHMJkkvRLIeynZuKqgyCJ4b6PnoNetZU4bUgvjLvrOcc9DbfWo7U9\nh3N/8GK+vNcTvd7p0jEDMf3t7UpVEm8XizGoa3baGITDU4jyfVmIhyjQOLCHf0I8wMmg1V5Jkruq\nQjWTFXa6Y47riTHHOZPVRXFXPXlgd8zfsC+wXLAqqfC5e3UFvn3NWDSsmlX4HeRy1xQPUHKntfce\n60E08M1Rjjn7OsesQEdZYuDVq17xB+/Wy6vG5x+Xnuuq0nmXVj3awzGlUsUxHFOMIceYy2dYXluC\nxuZJAxQ7IkXnOWwXHnWKm/52YXcjLvryQp1TMBUg3NkTfsFofj7YfCI6onVtxiB6zUwdV9hB9e1W\nhX3Nbfn36FlTCUjraxgXvsc/fwE27bMOZVf5f/PFJZNjLtdIzpDlxalwXYjVsMtw1Y9XFC1gJV8E\nrHcLVCX5qCD4s4IM+VE80r59zVhcPHoAfvniGqzYfsizXFAXuM4XkH5nYLh16ikgABeM7I8v/O1N\nDO3rnbPM6cXl/XTxOXzHL/a1eCuzGYXcjvlFOV9PAR+epHeyIZdCROYcJcWMviopdNWx4JhSJeUY\nXCNZ3pRGOTLTtdDA2fFek8lxLKWwuxF3Tl4Sg7yAh5EYanxUSToSg/iskXbuG5UKARCkI1+9uPqZ\n8jsN6V2Ls07ok2831SufbDPubtVpT391h7sqFewCYuQz77OT7fdaueOwJ/XT3jkGv/zAmZh8cj+H\nE4CqKf2SHvJAST8mZNURfozWVKbxjnHHBS40waok53fleejdqvCDd5+Oq8cfj8Z7rnYYo8X6//Vf\n50eSGHix/HiU7Em5nFWXSzuQ34A4+zgM8l6B6QKTiWIHUN3xx49NwG8/5Dz00tgYOgAKvuBCmECx\n/D3yRJWZjUeVKv9omQaXxJArXmLwUyXpeCWJaq8LTu6Pp784BTd7pKvmRXV2VfIkkF+poEKwGYOi\nDn5Aypjjeir02e4FgYiEA9oLZfn6fb4dF6BKu81RU5nG9WcNARHlddByfTrgeZaOtvtLDDqqJK99\nQhgbgt7vzgfdftWp2vfLKUj82svpBmz994p8zjGmnMd5SaGItTar8AqMkq1Y1c6Xjx2Ea8YPRo+a\nCt9yHYFjizEobAzywI7SETyJmuyNwSHvXDkNV552HG6dYKkhMoLE4DDISTttPjDlwRgm+tNfYvAe\nEnxBbpdE9NOH9vKUtAoSg3e75ndyHvdyyEZH1eSvqUznD0jRsjGgcJjO8b1r8+3L7x3apw5v3XkF\nPjH5RE/6ReQcC1jhQWIGVi/wQ35a2rO+5YrJrlrs2Rfy3fIxsarDabzul9Oc6+/grXJ8rGaZzBjU\nkr88zryeds149amIgGBjqIjGGHTUpgvuuLxQvoyT6HVqfHZ8wadbZWOQEcETEN2rK7DkO1PxTY/d\nEj+haeQAdz6h3tVcT6reJXp5JbkZQwivJB8bg1jtM1+agvunFvTDXCUQ5ixgHvMh7oJkeM0RT4mB\nG8FD7n7z5+2KXkkEvH/CMPzho+fgAxOGuRgDYKlGdFWMXl5JD31iIt7+zlTfe+vs1M5H24IYQ7jF\nXTa2+yH4d2eB43vV4rHPnq9Ny3LBvmF5gTnr+9rlo/PuyiLETcJA+xS1QhyDQmJQ9heXGP1p/PUH\nz8LaH7xT+ZtKYojCav02oKLXoJEYEsL5gyvwoxtOBwCcd2I/l8TgDjqL1hE9ayrzC5a8RA/rW4d/\n/df5+N71bs8HvsZmsuJgFvSu0iKQ9diBh1EliQv7QmF38t9TRzsm6mlDejny93zv+tPwk/eOz+eN\n0QHfYfWu8/bc8PbUcL4T/7kgOfg/29tNWCqXIkv/Ltzgx8j84GVjqKpI+Z7UBRTiPo7GKDEM6V2L\ne94zPv9dbpKT+nfD+ycUUpUEq5Lc13gSw7NP8Fa3cYgnoFmqJOfvX7l8lGcqEwC46fzh+QzAogea\nCNFLSbYn2Z9cv4lIpcjTFpT3CuRxLinvNCocf7ppQl7VGnZ5Me6qCeK6MweDALz3nKHYb2epvOWK\n0fj5C6tx1gm98dLKXfmyXjvDp784BY17m32fo7qVT5ZzhvfFm5v2A3Aud3nGkGN4+esX4+2tBx0l\nvCQGtz5enzGIA7mfkM75i5eqoy85ulVX4P0ThvmW8UJvjdO5ZDWH7B7K6S54RwXUp6FKkpttaJ9a\n3PqOU5TZanUgatnCzmkeMBYkMYQxPn/v+nGOw3fkNvnDR8/BqEGF/FZ8XF17hvr9vdp0xXev1KLr\n5gtG5NNhi9lsgxZM35gV6cccK8yb2sp0XmqVH1GU8dmeuLWV6UDaLzt1EIb3q8OfX2sMHUNRKonh\nmGAMdVUV+KB9nN+AHtVovOdqAFZgysgB3fHT51fny3p1xOlDe+H0od75W4DCQOO7RnmynDa4F94x\nbhC+JuRFERf+kwZ0x0kDumPG0u35a7JkcMkpA7Fo8wEc38u5aHqdVlUu8Du2kVwfLAzrW4dXb7sE\nf5q9AX9+rdFlrwhWJXk8T+hjecEhosCIaz9cPHpAcCEPcInBS1rhMRdh3FWDzjCX9fPplBXI17tW\nLeF5tamfQ4Pz+YI0nCqobqMsgFy6l4M7maBKqhEYgy4T8oMcx6D73n7xG34oEV84NhiDF8SdEgcf\nUN+4cgzOHaGvMgHcnS0PmqqKlOscX7W6noTfnXV+6dKTceOkYa7dtDzBdXDluPARm1ERxfgMWMxh\n7a4mAEDj3iOOuoLEbFn64y3US/Ns4Sioqkhh6thBeH75ztASQ7/u1fjhu0/HxaeomQvBegcvVdLH\nznf74rsYg/R7RpFLxS+QL86Aq2F967B5/1EAEb0BfbySOMMR03boGp/9MGZQDyzefCCfubWmIq1V\nj6wu5f8Dx7CRGMoDfIAO7VOLCSP6BpRWI8wSHXTwmeyVlEqRcuKGsTEAyEtNSWPyyf0wZ+1e3zJB\nnjILN+53fFedC6GC1883TjwBdzyxFEC45IO64DVGmdMfmnSC528p27XW672/e53bhiWXlRf2MCpI\nIJ5jRf976mg0t2VRLSQt1G0r50FItsQgG59zhXksOloU1DjR3+FuO338jkMtAID2XE6L9oLjg5P2\n4DFsGEPJcELfurxBVvaRDoMoXaiSGMRskrpRlUkscHHg4U9M0pZmvObA8H51ygCzqDaGdIowpHct\nth44qkVXWOi87lNfnIz1u/1tVjKsRYKFtCdJ36Xfw24ouOdUMRBtWZyeoHGuemWuppVParMkhoKN\nIf8sWWKIMGFrKtOYeGJfzFm7BwDQGhBz4gX+6Kibm6TR5b2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb30c7d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.67 and accuracy of 0.777\n",
      "\n",
      "\n",
      "Training epoch11 \n",
      "Iteration 0: with minibatch training loss = 0.519 and accuracy of 0.81\n",
      "Iteration 500: with minibatch training loss = 0.379 and accuracy of 0.86\n",
      "Epoch 1, Overall loss = 0.451 and accuracy of 0.843\n"
     ]
    },
    {
     "data": {
      "image/png": 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I3AYu79WzS5kyVNXZ10xV5/by5z/ajO9dUJj0FuX58ysQOTmmvnjqUNxz2RgA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb21e5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.651 and accuracy of 0.775\n",
      "\n",
      "\n",
      "Training epoch12 \n",
      "Iteration 0: with minibatch training loss = 0.524 and accuracy of 0.86\n",
      "Iteration 500: with minibatch training loss = 0.465 and accuracy of 0.88\n",
      "Epoch 1, Overall loss = 0.403 and accuracy of 0.861\n"
     ]
    },
    {
     "data": {
      "image/png": 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cX0wF4NZVigKecd/1z8X43XtrMOOuCzxt8rnVmbQoGNRtk/YbAWKEVW7jpZ2c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\n4MtDPb9tmn1oeSzprjwbfT5VsxbYASwnjQLfMrP3amjYo8/Xvw1MbKK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb1c31b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.637 and accuracy of 0.787\n",
      "\n",
      "\n",
      "Training epoch13 \n",
      "Iteration 0: with minibatch training loss = 0.256 and accuracy of 0.91\n",
      "Iteration 500: with minibatch training loss = 0.453 and accuracy of 0.83\n",
      "Epoch 1, Overall loss = 0.354 and accuracy of 0.878\n"
     ]
    },
    {
     "data": {
      "image/png": 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AbARQyxjjit4dsAzcsnvvBnA3AAwdOhQ1NTUmVQbQ0NAQuFf83dJsuZLOmz8f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\nwWZRAx5F+wcfg6pJT7RDiSikjU1dq03gJHiMCFFy6F6VgTj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b7bal4vjoXy8i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\nQVEURUlCjYOiKIqShBoHRVEUJYn/BwLkJkpC4QqMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb27e20b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.577 and accuracy of 0.819\n",
      "\n",
      "\n",
      "Training epoch14 \n",
      "Iteration 0: with minibatch training loss = 0.336 and accuracy of 0.92\n",
      "Iteration 500: with minibatch training loss = 0.442 and accuracy of 0.84\n",
      "Epoch 1, Overall loss = 0.315 and accuracy of 0.893\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hfy/YhCvunYtH3toQurZo4x7c+eJyAMmMz1ESQ6GpIzxG5f2HSK7F1aotbar+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\nXmBuHh+d51MLMx0E3A+cACzL/xRqkS0vp4udC0Mt1i2wT/7Hpjrm65b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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb22de6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.637 and accuracy of 0.808\n",
      "\n",
      "\n",
      "Training epoch15 \n",
      "Iteration 0: with minibatch training loss = 0.327 and accuracy of 0.89\n",
      "Iteration 500: with minibatch training loss = 0.342 and accuracy of 0.89\n",
      "Epoch 1, Overall loss = 0.27 and accuracy of 0.907\n"
     ]
    },
    {
     "data": {
      "image/png": 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KCXJ3LlacQ65wVZ/R36GsYuqRgoRu/dsan4t7kM0hF2w/ZDFAef3po4wuWpgY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\n+/duDl9/tsL3Pm0OiVIOMe6KPycv25YeuYu0p48P+W85PQf3dPp7XiM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c0c762748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.593 and accuracy of 0.808\n",
      "\n",
      "\n",
      "Training epoch16 \n",
      "Iteration 0: with minibatch training loss = 0.247 and accuracy of 0.91\n",
      "Iteration 500: with minibatch training loss = 0.317 and accuracy of 0.89\n",
      "Epoch 1, Overall loss = 0.232 and accuracy of 0.921\n"
     ]
    },
    {
     "data": {
      "image/png": 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HASyEIV14smoR0d0A7gaA8ePHI5/Ph2533k7DF3/lhu3ID9rrOb9lq+F10tZdxF8X78Tb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\nlqMx5ClkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6bb23d7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation\n",
      "Epoch 1, Overall loss = 0.594 and accuracy of 0.822\n",
      "\n",
      "\n",
      "Training epoch17 \n",
      "Iteration 0: with minibatch training loss = 0.112 and accuracy of 0.98\n",
      "Iteration 500: with minibatch training loss = 0.423 and accuracy of 0.81\n"
     ]
    }
   ],
   "source": [
    "# Feel free to play with this cell\n",
    "# This default code creates a session\n",
    "# and trains your model for 10 epochs\n",
    "# then prints the validation set accuracy\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for i in range(0,101):\n",
    "    print('Training epoch%d '%(i+1))\n",
    "    run_model(sess,y_out,mean_loss,X_train,y_train,1,64,500,train_step,True)\n",
    "    print('Validation')\n",
    "    run_model(sess,y_out,mean_loss,X_val,y_val,1,64)\n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "Epoch 1, Overall loss = 0.00123 and accuracy of 1\n",
      "Validation\n",
      "Epoch 1, Overall loss = 0.829 and accuracy of 0.852\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.82898137760162349, 0.85199999999999998)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test your model here, and make sure \n",
    "# the output of this cell is the accuracy\n",
    "# of your best model on the training and val sets\n",
    "# We're looking for >= 70% accuracy on Validation\n",
    "print('Training')\n",
    "run_model(sess,y_out,mean_loss,X_train,y_train,1,64)\n",
    "print('Validation')\n",
    "run_model(sess,y_out,mean_loss,X_val,y_val,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Describe what you did here\n",
    "In this cell you should also write an explanation of what you did, any additional features that you implemented, and any visualizations or graphs that you make in the process of training and evaluating your network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Tell us here_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Set - Do this only once\n",
    "Now that we've gotten a result that we're happy with, we test our final model on the test set. This would be the score we would achieve on a competition. Think about how this compares to your validation set accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test\n",
      "Epoch 1, Overall loss = 0.932 and accuracy of 0.829\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.93189846858978276, 0.82850000000000001)"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Test')\n",
    "run_model(sess,y_out,mean_loss,X_test,y_test,1,64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going further with TensorFlow\n",
    "\n",
    "The next assignment will make heavy use of TensorFlow. You might also find it useful for your projects. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
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